A Multisensor Data Fusion Approach for Simultaneous Localization and Mapping
| Autor: | Neveen Shlayan, Carl Sable, Zhekai Jin, Minjoon So, Dirk M. Luchtenburg, Yifei Shao |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
GeneralLiterature_INTRODUCTORYANDSURVEY Computer science business.industry 010401 analytical chemistry Robotics Ranging 02 engineering and technology Simultaneous localization and mapping Sensor fusion 01 natural sciences 0104 chemical sciences 020901 industrial engineering & automation Lidar Odometry Code (cryptography) Computer vision Artificial intelligence business |
| Zdroj: | ITSC |
| Popis: | Simultaneous localization and mapping (SLAM) has been an emerging research topic in the fields of robotics, autonomous driving, and unmanned aerial vehicles over the past thirty years. State of the art SLAM research is often inaccessible for undergraduate student researchers due to expensive hardware and difficult software setup. We present a cost-friendly vehicle research platform and a robust implementation of SLAM. Our SLAM algorithm fuses visual stereo image and 2D light detection and ranging (Lidar) data and uses loop closure for accurate odometry estimation. Our algorithm is benchmarked against other popular SLAM algorithms using the publicly available KITTI dataset and shown to be very accurate. For educational purposes, we publicly share the models and code presented in this work*. |
| Databáze: | OpenAIRE |
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