Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping

• URL: http://arxiv.org/abs/2002.01921v1
• Date: Wed, 5 Feb 2020 18:54:07 GMT
• Title: Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
• Authors: Thai Duong, Nikhil Das, Michael Yip, Nikolay Atanasov
• Abstract summary: 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.
• Score: 19.169233494235314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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