Stochastic Occupancy Grid Map Prediction in Dynamic Scenes

Zhanteng Xie, Philip Dames

Keywords: Environment Prediction, Probabilistic Inference, Robot Learning

TL;DR: We use techniques from generative AI to create a probabilistic prediction of the future to improve mobile robot navigation in dynamic spaces

Abstract: This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a range of possible future states of the environment. The algorithm takes full advantage of the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene to improve prediction accuracy. Three simulated and real-world datasets collected by different robot models are used to demonstrate that the proposed algorithm is able to achieve more accurate and robust prediction performance than other prediction algorithms. Furthermore, a predictive uncertainty-aware planner is proposed to demonstrate the effectiveness of the proposed predictor in simulation and real-world navigation experiments. Implementations are open source at https://github.com/TempleRAIL/SOGMP.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Video: https://youtu.be/lRxuADBL32U

Website: https://openreview.net/forum?id=fSmkKmWM5Ry

Code: https://github.com/TempleRAIL/SOGMP

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/stochastic-occupancy-grid-map-prediction-in/code)