スパースベイズカーネル占有マップによる未知環境の自律ナビゲーション
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
占有・自由空間の境界を機械学習の決定境界として捉え、Relevance Vector Machineに基づくスパースなベイズ占有マップをオンライン構築し、未知環境での自律ナビゲーションと衝突判定を実現した論文。
著者: Thai Duong, Michael Yip, Nikolay Atanasov
分類: cs.RO
原文アブストラクト
This paper 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, allowing robustness to measurement noise and probabilistic occupancy classification, supporting autonomous navigation. We provide an online training algorithm, updating the sparse Bayesian map incrementally from streaming range data, and an efficient collision-checking method for general curves, representing potential robot trajectories. The effectiveness of our mapping and collision checking algorithms is evaluated in tasks requiring autonomous robot navigation and active mapping in unknown environments.
