スパースカーネル占有マッピングによる未知環境の自律ナビゲーション
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
カーネルパーセプトロンの決定境界で占有・自由空間を分ける新しい地図表現を提案し、疎なサポートベクタで障害物境界を表現して衝突判定を完全に行う自律ナビゲーション手法を開発した。
著者: Thai Duong, Nikhil Das, Michael Yip, Nikolay Atanasov
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments.
