3D normal distributions transform occupancy maps: An efficient representation for mapping in dynamic environments

In order to enable long-term operation of autonomous vehicles in industrial environments numerous challenges need to be addressed. A basic requirement for many applications is the creation and maintenance of consistent 3D world models. This article proposes a novel 3D spatial representation for online real-world mapping, building upon two known representations: normal distributions transform (NDT) maps and occupancy grid maps. The proposed normal distributions transform occupancy map (NDT-OM) combines the advantages of both representations; compactness of NDT maps and robustness of occupancy maps. One key contribution in this article is that we formulate an exact recursive updates for NDT-OMs. We show that the recursive update equations provide natural support for multi-resolution maps. Next, we describe a modification of the recursive update equations that allows adaptation in dynamic environments. As a second key contribution we introduce NDT-OMs and formulate the occupancy update equations that allow to build consistent maps in dynamic environments. The update of the occupancy values are based on an efficient probabilistic sensor model that is specially formulated for NDT-OMs. In several experiments with a total of 17 hours of data from a milk factory we demonstrate that NDT-OMs enable real-time performance in large-scale, long-term industrial setups.

[1]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[2]  Andrew W. Fitzgibbon,et al.  KinectFusion: Real-time dense surface mapping and tracking , 2011, 2011 10th IEEE International Symposium on Mixed and Augmented Reality.

[3]  Paul Newman,et al.  Adaptive compression for 3D laser data , 2011, Int. J. Robotics Res..

[4]  Olivier Aycard,et al.  3D Mapping of Outdoor Environment Using Clustering Techniques , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[5]  Achim J. Lilienthal,et al.  Fast and accurate scan registration through minimization of the distance between compact 3D NDT representations , 2012, Int. J. Robotics Res..

[6]  Tom Duckett,et al.  A comparison of 3D registration algorithms for autonomous underground mining vehicles , 2005 .

[7]  F. Ramos,et al.  Unsupervised online learning for long-term autonomy , 2013, Int. J. Robotics Res..

[8]  Marco Pavone,et al.  An asymptotically-optimal sampling-based algorithm for Bi-directional motion planning , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  G. Golub,et al.  Updating formulae and a pairwise algorithm for computing sample variances , 1979 .

[10]  Achim J. Lilienthal,et al.  Has somethong changed here? Autonomous difference detection for security patrol robots , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D‐NDT , 2007, J. Field Robotics.

[12]  Takashi Tsubouchi,et al.  A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Christian Laugier,et al.  Error-Driven Refinement of Multi-scale Gaussian Maps - Application to 3-D Multi-scale Map Building, Compression and Merging , 2009, ISRR.

[14]  Christian Laugier,et al.  Update Policy of Dense Maps: Efficient Algorithms and Sparse Representation , 2007, FSR.

[15]  Hugh F. Durrant-Whyte,et al.  Gaussian Process modeling of large scale terrain , 2009, 2009 IEEE International Conference on Robotics and Automation.

[16]  Wolfram Burgard,et al.  Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders , 2007, Robotics: Science and Systems.

[17]  Wolfram Burgard,et al.  A benchmark for the evaluation of RGB-D SLAM systems , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Frank Dellaert,et al.  Planning in the continuous domain: A generalized belief space approach for autonomous navigation in unknown environments , 2015, Int. J. Robotics Res..

[19]  Takeo Kanade,et al.  Ambler: an autonomous rover for planetary exploration , 1989, Computer.

[20]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[21]  Jari Saarinen,et al.  Normal Distributions Transform Occupancy Maps: Application to large-scale online 3D mapping , 2013, 2013 IEEE International Conference on Robotics and Automation.

[22]  Achim J. Lilienthal,et al.  Comparative Evaluation of the Consistency of Three-dimensional Spatial Representations used in Autonomous Robot Navigation , 2013, J. Field Robotics.

[23]  Charles E. Thorpe,et al.  Simultaneous localization and mapping with detection and tracking of moving objects , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[24]  Wolfram Burgard,et al.  Map building with mobile robots in dynamic environments , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[25]  P. Newman,et al.  Adaptive compression for 3 D laser data , 2011 .

[26]  Eric Nettleton,et al.  Gaussian process modeling of large-scale terrain , 2009 .

[27]  Peter Biber,et al.  The normal distributions transform: a new approach to laser scan matching , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[28]  Sebastian Scherer,et al.  3D Convolutional Neural Networks for landing zone detection from LiDAR , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[29]  Achim J. Lilienthal,et al.  Path planning in 3D environments using the Normal Distributions Transform , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Gene H. Golub,et al.  Algorithms for Computing the Sample Variance: Analysis and Recommendations , 1983 .

[31]  Joachim Hertzberg,et al.  Automatic construction of polygonal maps from point cloud data , 2010, 2010 IEEE Safety Security and Rescue Robotics.

[32]  Wolfram Burgard,et al.  Adaptive Non-Stationary Kernel Regression for Terrain Modeling , 2007, Robotics: Science and Systems.

[33]  Hans P. Moravec Sensor Fusion in Certainty Grids for Mobile Robots , 1988, AI Mag..

[34]  C.S. Tzafestas,et al.  Temporal Occupancy Grid for mobile robot dynamic environment mapping , 2007, 2007 Mediterranean Conference on Control & Automation.

[35]  Gaurav S. Sukhatme,et al.  Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments , 2005, Auton. Robots.

[36]  Pieter Abbeel,et al.  Autonomous Helicopter Aerobatics through Apprenticeship Learning , 2010, Int. J. Robotics Res..

[37]  Aisha Walcott Long-term robot mapping in dynamic environments , 2011 .

[38]  Wolfram Burgard,et al.  Multi-Level Surface Maps for Outdoor Terrain Mapping and Loop Closing , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[39]  Tom Duckett,et al.  Dynamic Maps for Long-Term Operation of Mobile Service Robots , 2005, Robotics: Science and Systems.

[40]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[41]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles , 2007 .

[42]  Maja J. Mataric,et al.  Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[43]  Wolfram Burgard,et al.  Learning predictive terrain models for legged robot locomotion , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  Sebastian Thrun,et al.  Learning Occupancy Grid Maps with Forward Sensor Models , 2003, Auton. Robots.

[45]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008, J. Field Robotics.

[46]  Martin Magnusson,et al.  The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection , 2009 .

[47]  Cesar Rivadeneyra,et al.  Probabilistic multi-level maps from LIDAR data , 2011, Int. J. Robotics Res..

[48]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008 .