2022/07/05 更新

写真a

カルバヨ セグラ アレックサンダー
CARBALLO SEGURA Alexander
CARBALLO SEGURA Alexander
所属
未来社会創造機構 モビリティ社会研究所 モビリティサービス研究部門 特任准教授
大学院担当
大学院情報学研究科
職名
特任准教授
連絡先
メールアドレス
通称等の別名
カルバヨ アレックサンダー
プロフィール
Alexander Carballo received his Bachelor degree in Computer Engineering from Costa Rica Institute of Technology in 1996. He worked as lecturer for the undergraduate program of the Department of Computer Science at Costa Rica Institute of Technology from 1996 to 2006. In 2006 he joined the Intelligent Robot Laboratory at University of Tsukuba as research student, where he obtained the Doctor of Engineering degree in Computer Science in 2011. He worked at the Research and Development department of Hokuyo Automatic Co. Ltd. from 2011 until 2017. He is currently appointed as designated Associate Professor at Nagoya University. His research interests include machine learning, autonomous vehicles, robot navigation, machine perception and sensor fusion.
外部リンク

学位 1

  1. 博士(工学) ( 2011年3月   筑波大学 ) 

研究キーワード 8

  1. 自律移動ロボット

  2. 自動運転

  3. 画像処理

  4. 機械学習

  5. 人工知能

  6. コンピュータ工学

  7. コンピュータネットワーク

  8. LiDAR

研究分野 3

  1. ものづくり技術(機械・電気電子・化学工学) / 通信工学

  2. 情報通信 / 知覚情報処理  / LiDAR, 画像処理

  3. 情報通信 / 知能ロボティクス

経歴 5

  1. 株式会社 ティアフォー   シニアリサーチ フェロー

    2018年1月 - 現在

      詳細を見る

    国名:日本国

  2. 名古屋大学   未来社会創造機構   特任准教授

    2017年7月 - 現在

      詳細を見る

    国名:日本国

  3. 大阪市立大学   大学院工学研究科   非常勤講師

    2016年10月 - 現在

      詳細を見る

    国名:日本国

  4. 北陽電機株式会社   研究開発

    2011年4月 - 2017年6月

      詳細を見る

    国名:日本国

  5. コスタリカ工科大学   講師

    1996年1月 - 2006年6月

      詳細を見る

    国名:コスタリカ共和国

学歴 3

  1. 筑波大学   システム情報工学研究科   コンピュータサイエンス専攻

    2007年4月 - 2011年3月

      詳細を見る

    国名: 日本国

    備考: 博士後期課程

  2. 筑波大学   システム情報工学研究科   コンピュータサイエンス専攻

    2006年4月 - 2007年3月

      詳細を見る

    国名: 日本国

    備考: 知能ロボット研究室 研究生

  3. コスタリカ工科大学   コンピュータ工学学部

    1991年2月 - 1995年12月

      詳細を見る

    国名: コスタリカ共和国

所属学協会 4

  1. Asia-Pacific Signal and Information Processing Association (APSIPA)

    2019年10月 - 現在

  2. IEEE Intelligent Transportation Systems Society (ITSS)

    2018年1月 - 現在

  3. 日本ロボット学会

    2009年4月 - 現在

  4. IEEE Robotics and Automation Society (RAS)

    2008年1月 - 現在

受賞 1

  1. 2019年度 Journal of Robotics and Mechatronics (JRM) 優秀論文賞

    2020年1月   富士技術出版株式会社  

 

論文 45

  1. A comparison of methods for sharing recognition information interventions to assist recognition in autonomous driving system

    Atsushi Kuribayashi, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda

    IEEE Intelligent Vehicles Symposium (IV)     2021年7月

  2. RSG-Net: Towards Rich Semantic Relationship Prediction for Intelligent Vehicle in Complex Environment 査読有り

    Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

    IEEE Intelligent Vehicles Symposium (IV)     2021年7月

  3. Learning Personalized Driver Models via Probabilistic Sequence-to-Sequence Approaches

    Naren Bao, Alexander Carballo, Kazuya Takeda

    IEEE Intelligent Vehicles Symposium (IV)     2021年7月

  4. Eagleye: A Lane-Level Localization Using Low-Cost GNSS/IMU

    Aoki Takanose, Yuki Kitsukawa, Junichi Meguro, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda

    IEEE Intelligent Vehicles Symposium (IV) Autoware – ROS-based OSS for Autonomous Driving Workshop     2021年7月

  5. Characterization of Multiple 3D LiDARs for Localization and Mapping Performance using the NDT Algorithm

    Alexander Carballo, Abraham Israel Monrroy Cano, David Robert Wong, Patiphon Narksri, Jacob Lambert, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

    IEEE Intelligent Vehicles Symposium (IV) Autoware – ROS-based OSS for Autonomous Driving Workshop     2021年7月

  6. Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles 査読有り

    Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda

    CoRR   abs/2011.13588 巻   2020年11月

     詳細を見る

    掲載種別:研究論文(学術雑誌)  

    Rich semantic information extraction plays a vital role on next-generation
    intelligent vehicles. Currently there is great amount of research focusing on
    fundamental applications such as 6D pose detection, road scene semantic
    segmentation, etc. And this provides us a great opportunity to think about how
    shall these data be organized and exploited.
    In this paper we propose road scene graph,a special scene-graph for
    intelligent vehicles. Different to classical data representation, this graph
    provides not only object proposals but also their pair-wise relationships. By
    organizing them in a topological graph, these data are explainable,
    fully-connected, and could be easily processed by GCNs (Graph Convolutional
    Networks). Here we apply scene graph on roads using our Road Scene Graph
    dataset, including the basic graph prediction model. This work also includes
    experimental evaluations using the proposed model.

    arXiv

    その他リンク: http://arxiv.org/pdf/2011.13588v1

  7. LIBRE: The Multiple 3D LiDAR Dataset 査読有り

    Alexander Carballo, Jacob Lambert, Abraham Monrroy, David Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

    2020 IEEE Intelligent Vehicles Symposium (IV)   abs/2003.06129 巻   頁: 823 - 830   2020年10月

     詳細を見る

    担当区分:筆頭著者   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    DOI: 10.1109/iv47402.2020.9304681

    arXiv

  8. Point Grid Map-Based Mid-To-Mid Driving without Object Detection 査読有り

    Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

    2020 IEEE Intelligent Vehicles Symposium (IV)     頁: 2044 - 2051   2020年10月

     詳細を見る

    掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    DOI: 10.1109/iv47402.2020.9304809

    Scopus

  9. Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning 査読有り

    Kyle Sama, Yoichi Morales, Hailong Liu, Naoki Akai, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

    IEEE Transactions on Vehicular Technology   69 巻 ( 9 ) 頁: 9315 - 9329   2020年9月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/tvt.2020.2980197

    Web of Science

    Scopus

  10. Person-Following Algorithm Based on Laser Range Finder and Monocular Camera Data Fusion for a Wheeled Autonomous Mobile Robot 査読有り 国際共著

    Elvira Chebotareva, Ramil Safin, Kuo-Hsien Hsia, Alexander Carballo, Evgeni Magid

    Lecture Notes in Computer Science   12336 巻   頁: 21 - 33   2020年9月

     詳細を見る

    掲載種別:論文集(書籍)内論文   出版者・発行元:Springer International Publishing  

    DOI: 10.1007/978-3-030-60337-3_3

    Scopus

  11. Performance Analysis of 10 Models of 3D LiDARs for Automated Driving 査読有り

    Jacob Lambert, Alexander Carballo, Abraham Monrroy Cano, Patiphon Narksri, David Wong, Eijiro Takeuchi, Kazuya Takeda

    IEEE Access   8 巻   頁: 131699 - 131722   2020年7月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Institute of Electrical and Electronics Engineers (IEEE)  

    © 2013 IEEE. Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers releasing new sensors regularly. With this increased variety of LiDARs, each with different properties such as number of laser emitters, resolution, field-of-view, and price tags, a more in-depth comparison of their characteristics and performance is required. This work compares 10 commonly used 3D LiDARs, establishing several metrics to assess their performance. Various outstanding issues with specific LiDARs were qualitatively identified. The accuracy and precision of individual LiDAR beams and accumulated point clouds are evaluated in a controlled environment at distances from 5 to 180 meters. Reflective targets were used to characterize intensity patterns and quantify the impact of surface reflectivity on accuracy and precision. A vehicle and pedestrian mannequin were also used as additional targets of interest. A thorough assessment of these LiDARs is given with their potential applicability for automated driving tasks. The data collected in these experiments and analysis tools are all shared openly.

    DOI: 10.1109/access.2020.3009680

    Web of Science

    Scopus

  12. Personalized Subjective Driving Risk: Analysis and Prediction 査読有り

    Naren Bao, Alexander Carballo, Chiyomi Miyajima, Eijiro Takeuchi, Kazuya Takeda

    Journal of Robotics and Mechatronics   32 巻 ( 3 ) 頁: 503 - 519   2020年6月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Fuji Technology Press Ltd.  

    Subjective risk assessment is an important technology for enhancing driving safety, because an individual adjusts his/her driving behavior according to his/her own subjective perception of risk. This study presents a novel framework for modeling personalized subjective driving risk during expressway lane changes. The objectives of this study are twofold: (i) to use ego vehicle driving signals and surrounding vehicle locations in a data-driven and explainable approach to identify the possible influential factors of subjective risk while driving and (ii) to predict the specific individual’s subjective risk level just before a lane change. We propose the personalized subjective driving risk model, a combined framework that uses a random forest-based method optimized by genetic algorithms to analyze the influential risk factors, and uses a bidirectional long short term memory to predict subjective risk. The results demonstrate that our framework can extract individual differences of subjective risk factors, and that the identification of individualized risk factors leads to better modeling of personalized subjective driving risk.

    DOI: 10.20965/jrm.2020.p0503

    Web of Science

    Scopus

  13. The PIX Moving KuaiKai: Building a Self-Driving Car in Seven Days 査読有り

    Vehicles, Drivers, and Safety     頁: 233 - 250   2020年5月

     詳細を見る

    掲載種別:論文集(書籍)内論文   出版者・発行元:De Gruyter  

    DOI: 10.1515/9783110669787-014

  14. Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform

    Alexander Carballo, Abraham Monrroy, David Wong, Patiphon Narksri, Jacob Lambert, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

    CoRR   abs/2004.01374 巻   2020年4月

     詳細を見る

    掲載種別:研究論文(学術雑誌)  

    In this work, we present a detailed comparison of ten different 3D LiDAR
    sensors, covering a range of manufacturers, models, and laser configurations,
    for the tasks of mapping and vehicle localization, using as common reference
    the Normal Distributions Transform (NDT) algorithm implemented in the
    self-driving open source platform Autoware. LiDAR data used in this study is a
    subset of our LiDAR Benchmarking and Reference (LIBRE) dataset, captured
    independently from each sensor, from a vehicle driven on public urban roads
    multiple times, at different times of the day. In this study, we analyze the
    performance and characteristics of each LiDAR for the tasks of (1) 3D mapping
    including an assessment map quality based on mean map entropy, and (2) 6-DOF
    localization using a ground truth reference map.

    arXiv

    その他リンク: http://arxiv.org/pdf/2004.01374v1

  15. A Survey of Autonomous Driving: Common Practices and Emerging Technologies 査読有り

    Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda

    IEEE Access   8 巻   頁: 58443 - 58469   2020年3月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/access.2020.2983149

    Web of Science

    Scopus

    arXiv

  16. Motion Analysis and Performance Improved Method for 3D LiDAR Sensor Data Compression 査読有り

    Chenxi Tu, Eijiro Takeuchi, Alexander Carballo, Chiyomi Miyajima, Kazuya Takeda

    IEEE Transactions on Intelligent Transportation Systems   22 巻 ( 1 ) 頁: 243 - 256   2019年12月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/tits.2019.2956066

    Web of Science

    Scopus

  17. 3D Map Optimization with Fully Convolutional Neural Network and Dynamic Local NDT 査読有り 国際共著

    Zebang Shen, Yichong Xu, Muchen Sun, Alexander Carballo, Qingguo Zhou

    2019 IEEE Intelligent Transportation Systems Conference (ITSC)     頁: 4404 - 4411   2019年10月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    Due to multi-path effects, GNSS-based localization methods are not always reliable in urban transportation scenes. To solve this problem, matching-based methods, which compare the real-time sensor data with a prior map, are widely used for urban autonomous driving. In these methods, high-precision noise-free 3D map plays an essential role in vehicle localization. This paper proposes a 3D map optimization framework to generate such map with high efficiency and low memory consumption. First, a deep learning based method is designed to automatically filter out non-map objects in mobile laser scans during mapping. Then, a method named dynamic local NDT is introduced for mapping and localization to improve efficiency and reduce memory usage. Furthermore, a road segmentation method is exploited for further optimization. The proposed framework only relies on LIDAR and GNSS-INS, which makes it simple and easily conducted. The mapping and positioning experimental results show that the proposed framework outperforms the conventional NDT method.

    DOI: 10.1109/itsc.2019.8917130

    Web of Science

    Scopus

  18. SECOND-DX: Single-model Multi-class Extension for Sparse 3D Object Detection 査読有り

    Yusuke Muramatsu, Yuki Tsuji, Alexander Carballo, Simon Thompson, Hiroyuki Chishiro, Shinpei Kato

    2019 IEEE Intelligent Transportation Systems Conference (ITSC)     頁: 2675 - 2680   2019年10月

     詳細を見る

    掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    DOI: 10.1109/itsc.2019.8917386

    Web of Science

    Scopus

  19. Personalized Safety-focused Control by Minimizing Subjective Risk 査読有り

    Naren Bao, Dongfang Yang, Alexander Carballo, Umit Ozguner, Kazuya Takeda

    2019 IEEE Intelligent Transportation Systems Conference (ITSC)     頁: 3853 - 3858   2019年10月

     詳細を見る

    掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    © 2019 IEEE. We propose a data-driven control framework for autonomous driving which combines learning-based risk assessment with personalized, safety-focused, predictive control. Different control strategies are used depending on the detected risk level of the driving situation (risky vs. non-risky). This requires a model which can understand the context of the driving situation. In addition, autonomous driving should also be able to provide various safe and comfortable driving styles customized for various users, which requires a modeling method that can capture individual driving preferences. To achieve this, we propose a novel vehicle control framework in which Model Predictive Control (MPC) is combined with a learning-based risk assessment model. Random Forest (RF) methods are trained to classify driving scenes as risky or not risky, while at the same time capturing individually preferred travel velocities. If driving scenes are classified as risky, then the Safety-focused Model Predictive Control (SMPC) system will be launched to generate control commands satisfying predetermined safety constraints, otherwise, Personalized Model Predictive Control (PMPC) is used instead to track the driver's individually preferred velocity. We demonstrate experimentally our control framework.

    DOI: 10.1109/itsc.2019.8917457

    Web of Science

    Scopus

  20. Training Engineers in Autonomous Driving Technologies using Autoware 査読有り

    Alexander Carballo, David Wong, Yoshiki Ninomiya, Shinpei Kato, Kazuya Takeda

    2019 IEEE Intelligent Transportation Systems Conference (ITSC)     頁: 3347 - 3354   2019年10月

     詳細を見る

    掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    DOI: 10.1109/itsc.2019.8917152

    Web of Science

    Scopus

  21. Real-Time Streaming Point Cloud Compression for 3D LiDAR Sensor Using U-Net 査読有り

    Chenxi Tu, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda

    IEEE Access   7 巻   頁: 113616 - 113625   2019年8月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Institute of Electrical and Electronics Engineers (IEEE)  

    DOI: 10.1109/access.2019.2935253

    Web of Science

    Scopus

  22. End-to-End Driving using Point Cloud Features 査読有り

    Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

    5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-Zero'19)     2019年6月

  23. Recognition Assistance Interface for Autonomous Vehicles 査読有り

    Atsushi Kuribayashi, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda

    5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-Zero'19)     2019年6月

     詳細を見る

    掲載種別:研究論文(国際会議プロシーディングス)  

  24. Predicting pedestrian crossing intention using temporal contexts 査読有り

    Tajinder Singh, Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

    5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-Zero'19)     2019年6月

  25. Point Cloud Compression for 3D LiDAR Sensor using Recurrent Neural Network with Residual Blocks 査読有り

    Chenxi Tu, Eijiro Takeuchi, Alexander Carballo, Kazuya Takeda

    2019 International Conference on Robotics and Automation (ICRA)     頁: 3274 - 3280   2019年5月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    The use of 3D LiDAR, which has proven its capabilities in autonomous driving systems, is now expanding into many other fields. The sharing and transmission of point cloud data from 3D LiDAR sensors has broad application prospects in robotics. However, due to the sparseness and disorderly nature of this data, it is difficult to compress it directly into a very low volume. A potential solution is utilizing raw LiDAR data. We can rearrange the raw data from each frame losslessly in a 2D matrix, making the data compact and orderly. Due to the special structure of 3D LiDAR data, the texture of the 2D matrix is irregular, in contrast to 2D matrices of camera images. In order to compress this raw, 2D formatted LiDAR data efficiently, in this paper we propose a method which uses a recurrent neural network and residual blocks to progressively compress one frame's information from 3D LiDAR. Compared to our previous image compression based method and generic octree point cloud compression method, the proposed approach needs much less volume while giving the same decompression accuracy. Potential application scenarios for point cloud compression are also considered in this paper. We describe how decompressed point cloud data can be used with SLAM (simultaneous localization and mapping) as well as for localization using a given map, illustrating potential uses of the proposed method in real robotics applications.

    DOI: 10.1109/icra.2019.8794264

    Web of Science

    Scopus

  26. End-to-End Navigation with Branch Turning Support Using Convolutional Neural Network 査読有り

    Shunya Seiya, Alexander Carballo, Eijiro Takeuchi, Chiyomi Miyajima, Kazuya Takeda

    2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)     頁: 499 - 506   2018年12月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    © 2018 IEEE. End-to-end navigation refers to methods of generating control signals for mobile autonomous devices directly from external sensors, which is an area gaining attention within the autonomous driving research community. Previous autonomous driving research has mostly been focused on keeping moving vehicles within their lanes, but reliable navigation along other trajectories, including branching off onto other routes, has not yet been achieved. In this study we propose a deep learning system for end-to-end navigation which would allow an autonomous vehicle to turn at intersections. Our system's inputs include camera images and directions to a target, while the outputs are the steering control signals needed to direct the vehicle. We validate the system's performance by conducting experiments involving three different driving scenarios: short, indoor trajectories containing a single branching turn; long, outdoor trajectories containing many branching turns; and long, outdoor trajectories which were not included during training. Our end-to-end navigation system allowed an autonomous robot to successfully follow outdoor trajectories with right and left turns, including those which were not part of the training course.

    DOI: 10.1109/robio.2018.8665079

    Web of Science

    Scopus

  27. High Density Ground Maps using Low Boundary Height Estimation for Autonomous Vehicles 査読有り

    Alexander Carballo, Eijiro Takeuchi, Kazuya Takeda

    2018 21st International Conference on Intelligent Transportation Systems (ITSC)   2018-November 巻   頁: 3811 - 3818   2018年11月

     詳細を見る

    担当区分:筆頭著者   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    © 2018 IEEE. In this work we propose a new method to create high density ground maps for autonomous vehicles based on low boundary elevation estimation. Ground maps are created using ray casting of laser beams in 3D LiDAR data accumulated over time and computing the lower elevation data between 3D LiDAR scans. This lower boundary works as a low envelope of the accumulated point cloud. Our lower boundary ground maps approach is not affected by moving objects on the road, and produces high density maps even with coarse vertical resolution 3D LiDARs, works on long curved roads with different elevations, and it is computationally efficient. As proof of concept, we present an application for real-time obstacle and ground segmentation of 3D LiDAR data using our ground maps.

    DOI: 10.1109/itsc.2018.8569764

    Web of Science

    Scopus

  28. The PIX Moving Kuaikai – Building a Self-Driving Car in 7 Days 査読有り

    David Robert Wong, Alexander Carballo, Rohan Rao, Oscar Rovira, Chuan Yu

    8th Biennial Workshop on DSP in Vehicles     頁: O2-2   2018年10月

  29. End-to-End Autonomous Mobile Robot Navigation with Model-Based System Support 査読有り

    Alexander Carballo, Shunya Seiya, Jacob Lambert, Hatem Darweesh, Patiphon Narksri, Luis Yoichi Morales, Naoki Akai, Eijiro Takeuchi, Kazuya Takeda

    Journal of Robotics and Mechatronics   30 巻 ( 4 ) 頁: 563 - 583   2018年8月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Fuji Technology Press Ltd.  

    Autonomous mobile robot navigation in real unmodified outdoor areas frequented by people on their business, children playing, fast running bicycles, and even robots, remains a difficult challenge. For eleven years, the Tsukuba Challenge Real World Robot Challenge (RWRC) has brought together robots, researchers, companies, government, and ordinary citizens, under the same outdoor space to push forward the limits of autonomous mobile robots. For the Tsukuba Challenge 2017 participation, our team proposed to study the problem of sensors-to-actuators navigation (also called End-to-End), this is, having the robot to navigate towards the destination on a complex path, not only moving straight but also turning at intersections. End-to-End (E2E) navigation was implemented using a convolutional neural network (CNN): the robot learns how to go straight, turn left, and turn right, using camera images and trajectory data. E2E network training and evaluation was performed at Nagoya University, on similar outdoor conditions to that of Tsukuba Challenge 2017 (TC2017). Even thought E2E was trained on a different environment and conditions, the robot successfully followed the designated trajectory in the TC2017 course. Learning how to follow the road no matter the environment is of the key attributes of E2E based navigation. Our E2E does not perform obstacle avoidance and can be affected by illumination and seasonal changes. Therefore, to improve safety and add fault tolerance measures, we developed an E2E navigation approach with model-based system as backup. The model-based system is based on our open source autonomous vehicle software adapted to use on a mobile robot. In this work we describe our approach, implementation, experiences and main contributions.

    DOI: 10.20965/jrm.2018.p0563

    Web of Science

    Scopus

  30. Tsukuba Challenge 2017 Dynamic Object Tracks Dataset for Pedestrian Behavior Analysis 査読有り

    Jacob Lambert, Leslie Liang, Luis Yoichi Morales, Naoki Akai, Alexander Carballo, Eijiro Takeuchi, Patiphon Narksri, Shunya Seiya, Kazuya Takeda

    Journal of Robotics and Mechatronics   30 巻 ( 4 ) 頁: 598 - 612   2018年8月

     詳細を見る

    掲載種別:研究論文(学術雑誌)   出版者・発行元:Fuji Technology Press Ltd.  

    Navigation in social environments, in the absence of traffic rules, is the difficult task at the core of the annual Tsukuba Challenge. In this context, a better understanding of the soft rules that influence social dynamics is key to improve robot navigation. Prior research attempts to model social behavior through microscopic interactions, but the resulting emergent behavior depends heavily on the initial conditions, in particular the macroscopic setting. As such, data-driven studies of pedestrian behavior in a fixed environment may provide key insight into this macroscopic aspect, but appropriate data is scarcely available. To support this stream of research, we release an open-source dataset of dynamic object trajectories localized in a map of 2017 Tsukuba Challenge environment. A data collection platform equipped with lidar, camera, IMU, and odometry repeatedly navigated the challenge’s course, recording observations of passersby. Using a background map, we localized ourselves in the environment, removed the static background from the point cloud data, clustered the remaining points into dynamic objects and tracked their movements over time. In this work, we present the Tsukuba Challenge Dynamic Object Tracks dataset, which features nearly 10,000 trajectories of pedestrians, cyclists, and other dynamic agents, in particular autonomous robots. We provide a 3D map of the environment used as global frame for all trajectories. For each trajectory, we provide at regular time intervals an estimated position, velocity, heading, and rotational velocity, as well as bounding boxes for the objects and segmented lidar point clouds. As additional contribution, we provide a discussion which focuses on some discernible macroscopic patterns in the data.

    DOI: 10.20965/jrm.2018.p0598

    Web of Science

    Scopus

  31. CNNを用いたEnd-to-Endナビゲーションシステムによるつくばチャレンジへの取り組み

    清谷竣也, CARBALLO Alexander, 竹内栄二朗, 宮島千代美, 宮島千代美, 武田一哉, 武田一哉

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)   18th 巻   2017年

     詳細を見る

  32. Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot 査読有り

    Alexander Carballo, Akihisa Ohya, Shin’ichi Yuta

    International Journal of Social Robotics   3 巻 ( 2 ) 頁: 167 - 186   2011年4月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(学術雑誌)   出版者・発行元:Springer Science and Business Media LLC  

    Reliable people detection is an important task in several areas like security, intelligent environments and human robot interaction. People detection does not depend only upon separation of static environment objects from those showing motion (hopefully humans), a reliable system should be able to detect static people even in cluttered environments.This work presents a reliable approach for people detection and position estimation using multiple layers of Laser Range Finders (LRF) on a mobile robot. Each layer combines two LRF sensors to scan around the robot's surroundings and are vertically separated to detect distinct parts of the human body. By using AdaBoost we create strong classifiers to detect body parts, candidate segments in each layer are fused for people detection, and we use simple data association to estimate their positions. Additionally, this work introduces laser reflection intensity as a novel property for people detection. First, we present a study of laser intensity and textiles, then introduce new intensity-based features for detection, and propose a method for segment separation using laser intensity. We provide a thorough evaluation of our multi-layered system though several experiments on a mobile robot.

    DOI: 10.1007/s12369-010-0086-3

    Web of Science

    Scopus

    J-GLOBAL

    その他リンク: http://link.springer.com/article/10.1007/s12369-010-0086-3/fulltext.html

  33. 二層配置レーザ距離センサを用いた移動ロボットのための人間検出に関する研究 査読有り

    カルバヨ アレックサンダー

    筑波大学博士 (工学)博士論文   甲第5686号 巻   2011年3月

     詳細を見る

    担当区分:筆頭著者, 責任著者   記述言語:英語   掲載種別:学位論文(博士)  

  34. People detection using range and intensity data from multi-layered Laser Range Finders 査読有り

    Alexander Carballo, Akihisa Ohya, Shin'ichi Yuta

    2010 IEEE/RSJ International Conference on Intelligent Robots and Systems     頁: 5849 - 5854   2010年10月

     詳細を見る

    担当区分:筆頭著者, 責任著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    Effective detection of people is a basic requirement for robot coexistence in human environments. In our previous work [1] we proposed a method for people detection and position estimation using multiple layers of Laser Range Finders (LRF) in a mobile robot. We extend our work by introducing laser reflection intensity as a novel feature for people detection, achieving significant improvement of detection rates. In concrete, we propose a method for calibration of laser intensity data, a method for segment separation using laser intensity, and introduce two new intensity-based features for people detection: the variance of laser intensity and the variance of intensity differences. We present experimental results that confirm the effectiveness of our multi-layered detection method including laser intensity.

    DOI: 10.1109/iros.2010.5649769

    Web of Science

    Scopus

  35. Laser reflection intensity and multi-layered Laser Range Finders for people detection 査読有り

    Alexander Carballo, Akihisa Ohya, Shin'ichi Yuta

    19th International Symposium in Robot and Human Interactive Communication     頁: 379 - 384   2010年9月

     詳細を見る

    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory.In this work we introduce the usage of reflection intensity data of Laser Range Finders (LRF) arranged in multiple layers for people detection. We use supervised learning to train strong classifiers including intensity-based features. Concretely, we propose a calibration method for laser intensity and introduce new intensity-based features for people detection which are combined with range-based features in a strong classifier using supervised learning. We provide experimental results to evaluate the effectiveness of these features.This work is an step towards of our main research project of developing a social autonomous mobile robot acting as member of a people group.

    DOI: 10.1109/roman.2010.5598657

    Web of Science

    Scopus

    J-GLOBAL

  36. Multiple people detection from a mobile robot using double layered laser range finders 査読有り

    Alexander Carballo, Akihisa Ohya, Shin'ichi Yuta

    IEEE International Conference on Robotics and Automation (ICRA) Workshop on People Detection and Tracking     頁: 94 - 100   2009年5月

     詳細を見る

    担当区分:筆頭著者  

  37. People Detection using Double Layered Multiple Laser Range Finders by a Companion Robot 査読有り

    Alexander Carballo, Akihisa Ohya, Shin’ichi Yuta

    Lecture Notes in Electrical Engineering   35 LNEE 巻   頁: 315 - 331   2009年3月

     詳細を見る

    担当区分:筆頭著者   掲載種別:論文集(書籍)内論文   出版者・発行元:Springer Berlin Heidelberg  

    Successful detection and tracking of people is a basic requirement to achieve a robot symbiosis in people daily life. Specifically, a mobile robot designed to follow people needs to keep track of people position through time, for it defines the robot's position and trajectory. This work proposes a new method people detection and position estimation from a mobile robot by fusion of multiple Laser Range Finders arranged in two layers. Sensors facing opposite directions in a single row (layer) are combined to produce a 360° representation of robot's surroundings, then data from every layer is further fused to create a 3D model of people and from there their position. The main problem of our research is an autonomous mobile robot acting as member of a people group moving in public areas, simple but accurate people detection and tracking is an important requirement. We present experimental results of fusion steps and people detection in an indoor environment. © 2009 Springer-Verlag Berlin Heidelberg.

    DOI: 10.1007/978-3-540-89859-7_22

    Scopus

    J-GLOBAL

  38. 屋外の雑然とした歩道での自律ロボットナビゲーション

    MORALES Yoichi, CARBALLO Alexander, TAKEUCHI Eijiro, ABURADANI Atsushi, TSUBOUCHI Takashi

    Journal of Field Robotics   26 巻 ( 8 ) 頁: 609 - 635   2009年

     詳細を見る

    記述言語:英語   掲載種別:研究論文(学術雑誌)  

    DOI: 10.1002/rob.20301

    Web of Science

    Scopus

    J-GLOBAL

  39. つくばチャレンジ 2008 における筑波大学知能ロボット研究室 「屋外組」 の取組み 査読有り

    坪内, Y. Morales, A. Carballo, 原, 油谷, 城吉, 廣澤, 鈴木, K. Mehrez, 山口, 澤田, 森川

    第 9 回 SICE システムインテグレーション部門講演会 (SI2008)     2008年12月

  40. 1Km autonomous robot navigation on outdoor pedestrian paths "Running the Tsukuba challenge 2007" 査読有り

    Yoichi MORALES, Eijiro TAKEUCHI, Alexander CARBALLO, Wataru TOKUNAGA, Hiroyasu KUNIYOSHI, Atsushi ABURADANI, Atsushi HIROSAWA, Yoshisada NAGASAKA, Yusuke SUZUKI, Takashi TSUBOUCHI

    2008 IEEE/RSJ International Conference on Intelligent Robots and Systems     頁: 22 - 26   2008年9月

     詳細を見る

    記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    This paper presents and describes the approach for achieving long distance autonomous navigation with a mobile robot on outdoor cluttered pedestrian paths. The task was to finish an event launched by the City of Tsukuba in Japan, called "Real World Robot Challenge", of navigating 1km autonomously in a real environment with real pedestrians and bicycles. The hardware, software and strategy for navigating in cluttered environments is explained. Moreover, the complementary functionality of the overall system where map-based and sensor-based navigation seamlessly change, is presented. The robustness of the system is validated with experimental results.

    DOI: 10.1109/iros.2008.4650584

    Web of Science

    Scopus

  41. Fusion of double layered multiple laser range finders for people detection from a mobile robot 査読有り

    Alexander Carballo, Akihisa Ohya, Shin'ichi Yuta

    2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems     頁: 677 - 682   2008年8月

     詳細を見る

    担当区分:筆頭著者   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    DOI: 10.1109/mfi.2008.4648023

    Web of Science

    Scopus

  42. Time synchronization between SOKUIKI sensor and host computer using timestamps 査読有り

    Alexander Carballo, Yoshitaka Hara, Hirohiko Kawata, Tomoaki Yoshida, Akihisa Ohya, Shin'ichi Yuta

    2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems     頁: 261 - 266   2008年8月

     詳細を見る

    担当区分:筆頭著者   掲載種別:研究論文(国際会議プロシーディングス)   出版者・発行元:IEEE  

    DOI: 10.1109/mfi.2008.4648075

    J-GLOBAL

  43. 筑波大学知能ロボット研究室 『屋外組』 における屋外走行ロボットのシステムインテグレーション 査読有り

    坪内, Y. Morales, 徳永, 竹内, A. Carballo, 城吉, 鈴木, 油谷, 廣澤

    第 8 回 SICE システムインテグレーション部門講演会 (SI2007)     2007年12月

  44. 時刻印によるSOKUIKIセンサとホストコンピュータの間の同期化 査読有り

    Alexander CARBALLO, Yoshitaka HARA, Hirohiko KAWATA, Tomoaki YOSHIDA, Akihisa OHYA, Shin'ichi YUTA

    日本機械学会ロボティクス・メカトロニクス講演会講演論文集(CD-ROM)   1P1-K05 巻   2007年11月

     詳細を見る

    担当区分:筆頭著者   記述言語:英語   掲載種別:研究論文(国際会議プロシーディングス)  

    DOI: 10.1109/MFI.2008.4648075

    Web of Science

    Scopus

    J-GLOBAL

  45. Developing a Web Caching architecture with configurable consistency: A proposal 査読有り

    Francisco J. Torres-Rojas, Esteban Meneses, Alexander Carballo

    WEBIST 2005 - 1st International Conference on Web Information Systems and Technologies, Proceedings     頁: 110 - 116   2005年1月

     詳細を見る

    担当区分:筆頭著者, 責任著者   掲載種別:研究論文(国際会議プロシーディングス)  

    In recent years, Web Caching has been considered one of the key areas to improve web usage efficiency. However, caching web objects proposes many considerations about the validity of the cache. Ideally, it would be valuable to have a consistent cache, where no invalid relationships among objects are held. Several alternatives have been offered to keep consistency in the web cache, each one being better in different situations and for diverse requirements. Usually, web cachers implement just one strategy for maintaining consistency, sometimes giving bad results if circumstances are not appropriate for such strategy. Given that, a web cacher where this policy can be adapted to different situations, will offer good results in an execution with changing conditions. A web caching architecture is proposed as a testbed for consistency models, allowing both timing and ordering issues to be considered.

    Scopus

▼全件表示

講演・口頭発表等 4

  1. LIBRE Dataset: A Study of Multiple 3D LiDARs Performance for Autonomous Driving 招待有り

    第2回自律移動体シームレス化研究会, 愛知県公益財団法人 科学技術交流財団  2020年12月3日 

     詳細を見る

    開催年月日: 2020年12月

    会議種別:口頭発表(一般)  

  2. Education in Autonomous Driving Technologies using Autoware

    Alexander Carballo

    IEEE Intelligent Vehicles Symposium (IV) Autoware – ROS-based OSS for Autonomous Driving Workshop  2020年10月23日 

     詳細を見る

    開催年月日: 2020年10月

    会議種別:口頭発表(一般)  

  3. LIBRE: The 3D LiDAR Dataset – leveraging access to LiDARs 招待有り

    Alexander Carballo, Jacob Lambert, Abraham Israel Monrroy Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

    Automotive LiDAR 2020, 3rd Annual Conference and Exhibition, Michigan, USA  2020年9月24日 

     詳細を見る

    開催年月日: 2020年9月

    会議種別:口頭発表(一般)  

  4. CNNを用いたEnd-to-Endナビゲーションシステムによるつくばチャレンジへの取り組み

    清谷竣也, CARBALLO Alexander, 竹内栄二朗, 宮島千代美, 宮島千代美, 武田一哉, 武田一哉

    計測自動制御学会システムインテグレーション部門講演会(CD-ROM)  2017年 

     詳細を見る

    開催年月日: 2017年