動的環境における確率的占有格子地図予測
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
変分オートエンコーダを用いて動的環境の将来状態を確率的に予測し、不確実性を考慮した計画法と組み合わせて移動ロボットのナビゲーション精度を向上させた研究。
著者: Zhanteng Xie, Philip Dames
分類: cs.RO
原文アブストラクト
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.
