Task-driven SLAM Benchmarking For Robot Navigation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912524004229120 |
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| author | Du, Yanwei Feng, Shiyu Cort, Carlton G. Vela, Patricio A. |
| author_facet | Du, Yanwei Feng, Shiyu Cort, Carlton G. Vela, Patricio A. |
| contents | A critical use case of SLAM for mobile assistive robots is to support localization during a navigation-based task. Current SLAM benchmarks overlook the significance of repeatability (precision), despite its importance in real-world deployments. To address this gap, we propose a task-driven approach to SLAM benchmarking, TaskSLAM-Bench. It employs precision as a key metric, accounts for SLAM's mapping capabilities, and has easy-to-meet implementation requirements. Simulated and real-world testing scenarios of SLAM methods provide insights into the navigation performance properties of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM operates at a level of precision comparable to LiDAR SLAM in typical indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_16573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Task-driven SLAM Benchmarking For Robot Navigation Du, Yanwei Feng, Shiyu Cort, Carlton G. Vela, Patricio A. Robotics A critical use case of SLAM for mobile assistive robots is to support localization during a navigation-based task. Current SLAM benchmarks overlook the significance of repeatability (precision), despite its importance in real-world deployments. To address this gap, we propose a task-driven approach to SLAM benchmarking, TaskSLAM-Bench. It employs precision as a key metric, accounts for SLAM's mapping capabilities, and has easy-to-meet implementation requirements. Simulated and real-world testing scenarios of SLAM methods provide insights into the navigation performance properties of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM operates at a level of precision comparable to LiDAR SLAM in typical indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots. |
| title | Task-driven SLAM Benchmarking For Robot Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2409.16573 |