Learning highly dynamic environments with stochastic variational inference

Abstract

Understanding the dynamics of urban environments is crucial for path planning and safe navigation. However, the dynamics might be extremely complex making learning the environment an unfathomable task. Within the methods available for learning dynamic environments, dynamic Gaussian process occupancy maps (DGPOM) are very attractive because they can produce spatially-continuous occupancy maps taking into account neighborhood information, and provide probabilistic estimates, naturally inferring the uncertainty of predictions. Despite these properties, they are extremely slow, especially in dynamic mapping where the parameters of the map have to be updated as new data arrive from range sensors such as LiDARs. In this work, we leverage recent advancements in stochastic variational inference (SVI) to quickly learn dynamic areas in an online fashion. Further, we propose an information-driven technique to “intelligently” select inducing points required for SVI without relying on any object trackers which essentially improves computational time as well as robustness. These long-term occupancy maps entertain all attractive properties of DGPOM while the learning process is significantly faster, yet accurate. Our experiments with both simulation and real robot data on road intersections show a significant improvement in speed while maintaining a comparable or better accuracy.