  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.
