Autonomous navigation in unknown environments with sparse bayesian kernel-based occupancy mapping

IEEE Transactions on Robotics, 2022

Thai Duong, Michael Yip, Nikolay Atanasov

Publisher Link: http://ieeexplore.ieee.org/abstract/document/9798705/

Abstract: This article focuses on online occupancy mapping and real-time collision checking onboard an autonomous robot navigating in a large unknown environment. Commonly used voxel and octree map representations can be easily maintained in a small environment but have increasing memory requirements as the environment grows. We propose a fundamentally different approach for occupancy mapping, in which the boundary between occupied and free space is viewed as the decision boundary of a machine learning classifier. This work generalizes a kernel perceptron model which maintains a very sparse set of support vectors to represent the environment boundaries efficiently. We develop a probabilistic formulation based on relevance vector machines, handling measurement noise, and probabilistic occupancy classification, supporting autonomous navigation. We provide an online training algorithm, updating …

Duong et al. (2022) Autonomous navigation in unknown environments with sparse bayesian kernel-based occupancy mapping, IEEE Transactions on Robotics, vol. 38, no. 6, pp. 3694-3712.