Summary of Robosense: Large-scale Dataset and Benchmark For Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments, by Haisheng Su et al.
RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured Environments
by Haisheng Su, Feixiang Song, Cong Ma, Wei Wu, Junchi Yan
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel dataset, called RoboSense, and a multi-sensor data collection platform for egocentric robot perception in complex environments. The platform combines camera, LiDAR, and fisheye sensors to provide a 360-degree view from the robot’s perspective. The dataset contains over 133,000 synchronized sensor readings with annotated 3D bounding boxes and IDs, covering more than 7,600 temporal sequences and 2,160 trajectories. This is a significant expansion of existing datasets for autonomous driving scenarios like KITTI and nuScenes. To facilitate future research development, the authors define six tasks and provide detailed analysis and benchmarks based on RoboSense. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special dataset called RoboSense to help robots understand their surroundings better. It uses three different sensors (camera, LiDAR, and fisheye) to get a full view of what’s around it. The dataset has lots of data from different scenarios and is much bigger than similar datasets used for self-driving cars. This will make it easier for researchers to work on making robots smarter. |
