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.

翻译：本文提出了两种新颖的随机预测算法变体，使移动机器人能够准确且鲁棒地预测复杂动态场景的未来状态。该算法利用变分自编码器预测环境的一系列可能未来状态，并充分利用机器人自身运动、动态物体运动以及场景中静态物体的几何结构来提升预测精度。通过三种由不同机器人模型采集的仿真与真实世界数据集，证明了所提算法相比其他预测算法能够实现更准确、更鲁棒的预测性能。此外，本文提出了一种预测不确定性感知的规划器，通过仿真与真实世界导航实验验证了所提预测器的有效性。相关实现代码已在https://github.com/TempleRAIL/SOGMP开源。