What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing
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Abstract (arXiv)
We consider the navigation of mobile robots in crowded environments, for which onboard sensing of the crowd is typically limited by occlusions. We address the problem of inferring the human occupancy in the space around the robot, in blind spots, beyond the range of its sensing capabilities. This problem is rather unexplored in spite of the important impact it has on the robot crowd navigation efficiency and safety, which requires the estimation and the prediction of the crowd state around it. In this work, we propose the first solution to sample predictions of possible human presence based on the state of a fewer set of sensed people around the robot as well as previous observations of the crowd activity.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 17 Sep 2021.
Builds on
7 of 24 references.
References · 7
- 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
- 2019Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANscited by 78
- 2017A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environmentscited by 23
- 2020OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasetscited by 20
- 2020Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environmentscited by 20
- 2020DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Datacited by 18
- 2019FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environmentscited by 10
Built on by
Citing papers per year: 2022: 1.
1 paper.
- 2022Occlusion-Aware Crowd Navigation Using People as Sensorscited by 8
- 2022Occlusion-Aware Crowd Navigation Using People as Sensors2 Oct 2022