Kyohei Otsu

1.1k total citations
26 papers, 492 citations indexed

About

Kyohei Otsu is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Biomedical Engineering. According to data from OpenAlex, Kyohei Otsu has authored 26 papers receiving a total of 492 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 12 papers in Aerospace Engineering and 6 papers in Biomedical Engineering. Recurrent topics in Kyohei Otsu's work include Robotics and Sensor-Based Localization (9 papers), Robotic Path Planning Algorithms (7 papers) and Modular Robots and Swarm Intelligence (5 papers). Kyohei Otsu is often cited by papers focused on Robotics and Sensor-Based Localization (9 papers), Robotic Path Planning Algorithms (7 papers) and Modular Robots and Swarm Intelligence (5 papers). Kyohei Otsu collaborates with scholars based in United States, Japan and Germany. Kyohei Otsu's co-authors include Ali‐akbar Agha‐mohammadi, Masahiro Ono, Takashi Kubota, Joel W. Burdick, Thomas J. Fuchs, Anushri Dixit, Ian Baldwin, David P. Fan, Y. Kubo and Rohan Thakker and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Robotics and Automation Letters and Advanced Robotics.

In The Last Decade

Kyohei Otsu

24 papers receiving 477 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Kyohei Otsu United States 13 262 220 94 81 78 26 492
Tim Barfoot Canada 12 258 1.0× 213 1.0× 127 1.4× 127 1.6× 64 0.8× 21 531
Christoforos Kanellakis Sweden 14 521 2.0× 552 2.5× 156 1.7× 122 1.5× 35 0.4× 66 816
Başaran Bahadır Koçer United Kingdom 12 173 0.7× 289 1.3× 288 3.1× 57 0.7× 28 0.4× 34 626
Ashley Stroupe United States 13 170 0.6× 171 0.8× 90 1.0× 123 1.5× 42 0.5× 24 429
Osman Parlaktuna Türkiye 11 268 1.0× 258 1.2× 173 1.8× 155 1.9× 32 0.4× 44 533
Loukas Petrou Greece 14 254 1.0× 225 1.0× 91 1.0× 59 0.7× 19 0.2× 38 519
C. Urmson United States 12 457 1.7× 349 1.6× 161 1.7× 51 0.6× 50 0.6× 15 718
Immacolata Notaro Italy 14 226 0.9× 301 1.4× 162 1.7× 126 1.6× 23 0.3× 39 529
Omead Amidi United States 10 548 2.1× 422 1.9× 230 2.4× 53 0.7× 60 0.8× 18 768
Kooktae Lee United States 10 133 0.5× 179 0.8× 130 1.4× 93 1.1× 34 0.4× 38 371

Countries citing papers authored by Kyohei Otsu

Since Specialization
Citations

This map shows the geographic impact of Kyohei Otsu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kyohei Otsu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kyohei Otsu more than expected).

Fields of papers citing papers by Kyohei Otsu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kyohei Otsu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kyohei Otsu. The network helps show where Kyohei Otsu may publish in the future.

Co-authorship network of co-authors of Kyohei Otsu

This figure shows the co-authorship network connecting the top 25 collaborators of Kyohei Otsu. A scholar is included among the top collaborators of Kyohei Otsu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Kyohei Otsu. Kyohei Otsu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Atha, Deegan, Seyed Fakoorian, Kyohei Otsu, et al.. (2022). Self-Supervised Traversability Prediction by Learning to Reconstruct Safe Terrain. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 12419–12425. 31 indexed citations
4.
Kim, Taeyeon, D. Pastor, Barry Ridge, et al.. (2022). Early Recall, Late Precision: Multi-Robot Semantic Object Mapping under Operational Constraints in Perceptually-Degraded Environments. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2017–2024. 5 indexed citations
5.
Fan, David D., et al.. (2022). PrePARE: Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4338–4343. 8 indexed citations
6.
Clark, Lillian, Jeffrey A. Edlund, Kyohei Otsu, et al.. (2022). ACHORD: Communication-Aware Multi-Robot Coordination With Intermittent Connectivity. IEEE Robotics and Automation Letters. 7(4). 10184–10191. 26 indexed citations
7.
Kim, Sung-Kyun, Gautam Salhotra, David D. Fan, et al.. (2021). PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments. Proceedings of the International Conference on Automated Planning and Scheduling. 31. 652–662. 34 indexed citations
8.
Ono, Masahiro, et al.. (2021). Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments. International Journal of Advanced Robotic Systems. 18(2). 19 indexed citations
9.
Ginting, Muhammad Fadhil, et al.. (2021). CHORD: Distributed Data-Sharing via Hybrid ROS 1 and 2 for Multi-Robot Exploration of Large-Scale Complex Environments. IEEE Robotics and Automation Letters. 6(3). 5064–5071. 27 indexed citations
10.
Sasaki, Takahiro, Kyohei Otsu, Rohan Thakker, Sofie Haesaert, & Ali‐akbar Agha‐mohammadi. (2020). Where to Map? Iterative Rover-Copter Path Planning for Mars Exploration. IEEE Robotics and Automation Letters. 5(2). 2123–2130. 29 indexed citations
11.
Otsu, Kyohei, Rohan Thakker, Tiago Vaquero, et al.. (2020). Supervised Autonomy for Communication-degraded Subterranean Exploration by a Robot Team. 1–9. 34 indexed citations
12.
Iwashita, Yumi, et al.. (2019). Vision-Based Estimation of Driving Energy for Planetary Rovers Using Deep Learning and Terramechanics. IEEE Robotics and Automation Letters. 4(4). 3876–3883. 37 indexed citations
13.
Nilsson, Petter, Sofie Haesaert, Rohan Thakker, et al.. (2018). Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams. 21 indexed citations
14.
Ono, Masahiro, Matthew Heverly, Brandon Rothrock, et al.. (2018). Mars 2020 Site-Specific Mission Performance Analysis: Part 2. Surface Traversability. 16 indexed citations
15.
Otsu, Kyohei, Ali‐akbar Agha‐mohammadi, & Michael Paton. (2017). Where to Look? Predictive Perception With Applications to Planetary Exploration. IEEE Robotics and Automation Letters. 3(2). 635–642. 21 indexed citations
16.
Otsu, Kyohei, Takao Maeda, Masatsugu Otsuki, & Takashi Kubota. (2016). A Study on Monocular Visual Odometry using Parabolic Motion Constraints. The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2016(0). 2A2–17b5.
17.
Otsu, Kyohei & Takashi Kubota. (2016). Estimating energy consumption based on natural terrain classification for mobile robots. SHILAP Revista de lepidopterología. 82(834). 15–399. 2 indexed citations
18.
Otsu, Kyohei & Takashi Kubota. (2014). Visual Odometry for Planetary Exploration Rovers in Untextured Terrains. Journal of the Robotics Society of Japan. 32(9). 825–831.
19.
Otsu, Kyohei, Masatsugu Otsuki, Genya Ishigami, & Takashi Kubota. (2013). Terrain adaptive detector selection for visual odometry in natural scenes. Advanced Robotics. 27(18). 1465–1476. 6 indexed citations
20.
Otsu, Kyohei, Masatsugu Otsuki, Genya Ishigami, & Takashi Kubota. (2012). 2A1-L10 A Study on Feature Selection Algorithm for Visual Odometry in Natural Terrain(Localization and Mapping(1)). The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec). 2012(0). _2A1–L10_1. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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