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This paper focuses on online occupancy mapping and real-time collisionchecking onboard an autonomous robot navigating in a large unknown environment.Commonly used voxel and octree map representations can be easily maintained ina small environment but have increasing memory requirements as the environmentgrows. We propose a fundamentally different approach for occupancy mapping, inwhich the boundary between occupied and free space is viewed as the decisionboundary of a machine learning classifier. This work generalizes a kernelperceptron model which maintains a very sparse set of support vectors torepresent the environment boundaries efficiently. We develop a probabilisticformulation based on Relevance Vector Machines, allowing robustness tomeasurement noise and probabilistic occupancy classification, supportingautonomous navigation. We provide an online training algorithm, updating thesparse Bayesian map incrementally from streaming range data, and an efficientcollision-checking method for general curves, representing potential robottrajectories. The effectiveness of our mapping and collision checkingalgorithms is evaluated in tasks requiring autonomous robot navigation andactive mapping in unknown environments.

Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping