  This article presents a family of Stochastic Cartographic Occupancy
Prediction Engines (SCOPEs) that enable mobile robots to predict the future
states of complex dynamic environments. They do this by accounting for the
motion of the robot itself, the motion of dynamic objects, and the geometry of
static objects in the scene, and they generate a range of possible future
states of the environment. These prediction algorithms are software-optimized
for real-time performance for navigation in crowded dynamic scenes, achieving
10 times faster inference speed and 3 times less memory usage than the original
engines. Three simulated and real-world datasets collected by different robot
models are used to demonstrate that these proposed prediction algorithms are
able to achieve more accurate and robust stochastic prediction performance than
other algorithms. Furthermore, a series of simulation and hardware navigation
experiments demonstrate that the proposed predictive uncertainty-aware
navigation framework with these stochastic prediction engines is able to
improve the safe navigation performance of current state-of-the-art model- and
learning-based control policies.
