Autonomous navigation in unknown environments using sparse kernel-based occupancy mapping

Proc. IEEE International Conference on Robotics and Automation (ICRA), 2020

Thai Duong, Nikhil Das, Michael Yip, Nikolay Atanasov

Abstract: 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.

Duong et al. (2020) Autonomous navigation in unknown environments using sparse kernel-based occupancy mapping, Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 9666-9672.

Pub Link: http://ieeexplore.ieee.org/abstract/document/9197412/
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