# OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors

arXiv: https://arxiv.org/abs/2004.10976 · HTML: https://mlsanity.com/paper/2004.10976/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models](https://mlsanity.com/paper/2404.00210/) (2024)
- [DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation](https://mlsanity.com/paper/2403.09900/) (2024)
- [VL-TGS: Trajectory Generation and Selection using Vision Language Models in Mapless Outdoor Environments](https://mlsanity.com/paper/2408.02454/) (2024)
- [AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales](https://mlsanity.com/paper/2404.03187/) (2024)
- [MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation](https://mlsanity.com/paper/2209.09079/) (2022)

## Abstract (arXiv)

We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estimation, object detection, and sensor fusion. A key aspect of our work is coupling the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance. Our method also provides bounds on the probabilistic collision avoidance algorithm. We highlight the realtime performance of OF-VO on a Turtlebot navigating among pedestrians in both simulated and real-world scenes. A demo video is available at https://gamma.umd.edu/ofvo/