论文标题
动态场景中的随机占用网格图预测
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes
论文作者
论文摘要
本文介绍了一种新型随机预测算法的两种变体，该算法使移动机器人能够准确，稳健地预测复杂动态场景的未来状态。所提出的算法使用变分自动编码器来预测一系列可能的未来环境状态。该算法充分利用了机器人本身的运动，动态对象的运动以及场景中静态对象的几何形状以提高预测准确性。不同机器人模型收集的三个模拟和现实世界数据集用于证明所提出的算法能够比其他预测算法实现更准确和强大的预测性能。此外，提出了一种预测性不确定性的计划者，以证明所提出的预测因子在仿真和现实导航实验中的有效性。实现是https://github.com/templerail/sogmp的开源。
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.
