# What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing

arXiv: https://arxiv.org/abs/2109.08494 · HTML: https://mlsanity.com/paper/2109.08494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs](https://mlsanity.com/paper/1904.09507/) (2019)
- [A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments](https://mlsanity.com/paper/1709.08528/) (2017)
- [OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets](https://mlsanity.com/paper/2010.00890/) (2020)
- [Learning-based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments](https://mlsanity.com/paper/2011.03981/) (2020)
- [DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data](https://mlsanity.com/paper/2004.14079/) (2020)
- [FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments](https://mlsanity.com/paper/1903.03558/) (2019)

## Built on by (most cited first, 1 of 1)

- [Occlusion-Aware Crowd Navigation Using People as Sensors](https://mlsanity.com/paper/2210.00552/) (2022)

## 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.