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.
@article{arxiv.2002.01921,
title = {Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping},
author = {Thai Duong and Nikhil Das and Michael Yip and Nikolay Atanasov},
journal= {arXiv preprint arXiv:2002.01921},
year = {2020}
}