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

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.

[1]  Kartik Mohta,et al.  Search-based motion planning for quadrotors using linear quadratic minimum time control , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Panagiotis Tsiotras,et al.  Machine learning guided exploration for sampling-based motion planning algorithms , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Sean L. Bowman,et al.  Probabilistic data association for semantic SLAM , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[5]  Daniel D. Lee,et al.  Learning high-dimensional Mixture Models for fast collision detection in Rapidly-Exploring Random Trees , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[7]  Fabio Tozeto Ramos,et al.  Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent , 2015, Robotics: Science and Systems.

[8]  Emilio Frazzoli,et al.  Efficient collision checking in sampling-based motion planning via safety certificates , 2016, Int. J. Robotics Res..

[9]  Kris K. Hauser,et al.  An empirical study of optimal motion planning , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Michael C. Yip,et al.  Learning-Based Proxy Collision Detection for Robot Motion Planning Applications , 2019, IEEE Transactions on Robotics.

[11]  Michael Kaess,et al.  Simultaneous localization and mapping with infinite planes , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Francesco Amigoni,et al.  Standard for Robot Map Data Representation for Navigation , 2014, IROS 2014.

[13]  Cyrill Stachniss,et al.  Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments , 2018, Robotics: Science and Systems.

[14]  Avideh Zakhor,et al.  AtomMap: A probabilistic amorphous 3D map representation for robotics and surface reconstruction , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Shaojie Shen,et al.  Improving octree-based occupancy maps using environment sparsity with application to aerial robot navigation , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Dinesh Manocha,et al.  FCL: A general purpose library for collision and proximity queries , 2012, 2012 IEEE International Conference on Robotics and Automation.

[17]  Roland Siegwart,et al.  Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  Jonathan P. How,et al.  Aggressive 3-D collision avoidance for high-speed navigation , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[20]  Pan,et al.  Efficient Configuration Space Construction ant Dptimization for Motion Planning , 2015 .

[21]  Brendan Englot,et al.  Fast, accurate gaussian process occupancy maps via test-data octrees and nested Bayesian fusion , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Michael C. Yip,et al.  Fastron: An Online Learning-Based Model and Active Learning Strategy for Proxy Collision Detection , 2017, CoRL.

[23]  Emmanouil Tsardoulias,et al.  A Review of Global Path Planning Methods for Occupancy Grid Maps Regardless of Obstacle Density , 2016, J. Intell. Robotic Syst..

[24]  Alessandro De Luca,et al.  Stabilization of the Unicycle Via Dynamic Feedback Linearization , 2000 .

[25]  Kris Hauser,et al.  Lazy collision checking in asymptotically-optimal motion planning , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Fabio Tozeto Ramos,et al.  Gaussian process occupancy maps* , 2012, Int. J. Robotics Res..

[27]  Jaume Franch,et al.  Control and trajectory generation of an Ackerman vehicle by dynamic linearization , 2009, 2009 European Control Conference (ECC).

[28]  Daniel E. Koditschek,et al.  Exact robot navigation using power diagrams , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[29]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[30]  Luca Maria Gambardella,et al.  Human-friendly robot navigation in dynamic environments , 2013, 2013 IEEE International Conference on Robotics and Automation.

[31]  Jaime Valls Miró,et al.  Warped Gaussian Processes Occupancy Mapping With Uncertain Inputs , 2017, IEEE Robotics and Automation Letters.

[32]  Marco Pavone,et al.  Safe Motion Planning in Unknown Environments: Optimality Benchmarks and Tractable Policies , 2018, Robotics: Science and Systems.