Mapping Occupancy of Dynamic Environments using Big Data Gaussian Process Classification

Ransalu Senanayake, Simon O’Callaghan, Fabio Ramos

Oct 15, 2016 (modified: Oct 15, 2016) NIPS 2016 workshop MLITS submission readers: everyone
  • Abstract: Understanding the dynamics of urban environments is crucial for path planning and safe navigation. However, the dynamics might be extremely complex making mapping a non-trivial problem. Within the methods available for learning dynamic environments, Dynamic Gaussian process occupancy maps (DGPOM) are 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 tracker or velocity information. 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 as DGPOM.
  • Conflicts: uni.sydney.edu.au, data61.csiro.au, sydney.edu.au

Loading