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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.09755 |
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| _version_ | 1866916316109078528 |
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| author | Taamazyan, Vage Dall'olio, Alberto Kalra, Agastya |
| author_facet | Taamazyan, Vage Dall'olio, Alberto Kalra, Agastya |
| contents | 3D cameras have emerged as a critical source of information for applications in robotics and autonomous driving. These cameras provide robots with the ability to capture and utilize point clouds, enabling them to navigate their surroundings and avoid collisions with other objects. However, current standard camera evaluation metrics often fail to consider the specific application context. These metrics typically focus on measures like Chamfer distance (CD) or Earth Mover's Distance (EMD), which may not directly translate to performance in real-world scenarios. To address this limitation, we propose a novel metric for point cloud evaluation, specifically designed to assess the suitability of 3D cameras for the critical task of collision avoidance. This metric incorporates application-specific considerations and provides a more accurate measure of a camera's effectiveness in ensuring safe robot navigation. The source code is available at https://github.com/intrinsic-ai/collision-avoidance-metric. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09755 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Collision Avoidance Metric for 3D Camera Evaluation Taamazyan, Vage Dall'olio, Alberto Kalra, Agastya Computer Vision and Pattern Recognition Robotics 3D cameras have emerged as a critical source of information for applications in robotics and autonomous driving. These cameras provide robots with the ability to capture and utilize point clouds, enabling them to navigate their surroundings and avoid collisions with other objects. However, current standard camera evaluation metrics often fail to consider the specific application context. These metrics typically focus on measures like Chamfer distance (CD) or Earth Mover's Distance (EMD), which may not directly translate to performance in real-world scenarios. To address this limitation, we propose a novel metric for point cloud evaluation, specifically designed to assess the suitability of 3D cameras for the critical task of collision avoidance. This metric incorporates application-specific considerations and provides a more accurate measure of a camera's effectiveness in ensuring safe robot navigation. The source code is available at https://github.com/intrinsic-ai/collision-avoidance-metric. |
| title | Collision Avoidance Metric for 3D Camera Evaluation |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2405.09755 |