Robust Mapping and Localization in Indoor Environments Using Sonar Data

In this paper we describe a new technique for the creation of feature-based stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison.

[1]  Viii Supervisor.  Sonar-Based Real-World Mapping and Navigation , 2001 .

[2]  Whitlow W. L. Au,et al.  The Sonar of Dolphins , 1993, Springer New York.

[3]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building , 1999 .

[4]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[5]  J. M. M. Montiel,et al.  Indoor robot motion based on monocular images , 2001, Robotica.

[6]  Ingemar J. Cox,et al.  Modeling a Dynamic Environment Using a Bayesian Multiple Hypothesis Approach , 1994, Artif. Intell..

[7]  Olivier D. Faugeras,et al.  The geometry of multiple images - the laws that govern the formation of multiple images of a scene and some of their applications , 2001 .

[8]  Raja Chatila,et al.  Stochastic multisensory data fusion for mobile robot location and environment modeling , 1989 .

[9]  D. E. Davies,et al.  Array signal processing , 1983 .

[10]  Ian D. Reid,et al.  Towards constant time SLAM using postponement , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[11]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[12]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[13]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[14]  Lindsay Kleeman,et al.  Mobile-Robot Map Building from an Advanced Sonar Array and Accurate Odometry , 1999, Int. J. Robotics Res..

[15]  John J. Leonard,et al.  Adaptive Mobile Robot Navigation and Mapping , 1999, Int. J. Robotics Res..

[16]  Alan C. Schultz,et al.  Continuous localization using evidence grids , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[17]  Sebastian Thrun,et al.  A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots , 2001, Int. J. Robotics Res..

[18]  Andrew J. Davison,et al.  Mobile Robot Navigation Using Active Vision , 1998 .

[19]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .

[20]  David B. Fogel,et al.  Foreword I , 1990, Copyright in the Music Industry.

[21]  José A. Castellanos,et al.  Mobile Robot Localization and Map Building: A Multisensor Fusion Approach , 2000 .

[22]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[23]  Henrik I. Christensen,et al.  Triangulation-based fusion of sonar data with application in robot pose tracking , 2000, IEEE Trans. Robotics Autom..

[24]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[25]  O. Wijk.  Triangulation Based Fusion of Sonar Data with Application in Mobile Robot Mapping and Localization , 2001 .

[26]  Billur Barshan,et al.  Differentiating Sonar Reflections from Corners and Planes by Employing an Intelligent Sensor , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[28]  Patric Jensfelt,et al.  Approaches to Mobile Robot Localization in Indoor Environments , 2001 .

[29]  Evangelos E. Milios,et al.  Globally Consistent Range Scan Alignment for Environment Mapping , 1997, Auton. Robots.

[30]  Lindsay Kleeman,et al.  Feature-Based Mapping in Real, Large Scale Environments Using an Ultrasonic Array , 1999, Int. J. Robotics Res..

[31]  H. Christensen,et al.  Navigation in realistic environments , 2001 .

[32]  Olivier Faugeras,et al.  Maintaining representations of the environment of a mobile robot , 1988, IEEE Trans. Robotics Autom..

[33]  Roman Kuc,et al.  Physically Based Simulation Model for Acoustic Sensor Robot Navigation , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Simon J. Julier,et al.  A sparse weight Kalman filter approach to simultaneous localisation and map building , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[35]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[36]  Herbert Peremans,et al.  A high-resolution sensor based on tri-aural perception , 1993, IEEE Trans. Robotics Autom..

[37]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .

[38]  D. McDowell.  Foreword , 1999 .

[39]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[40]  Sebastian Thrun,et al.  An Online Mapping Algorithm for Teams of Mobile Robots , 2000 .

[41]  Benjamin Kuipers,et al.  The Spatial Semantic Hierarchy , 2000, Artif. Intell..

[42]  John J. Leonard,et al.  A Computationally Efficient Method for Large-Scale Concurrent Mapping and Localization , 2000 .

[43]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1997, J. Intell. Robotic Syst..

[44]  C. Burrus,et al.  Array Signal Processing , 1989 .

[45]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[46]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[47]  Peter Cheeseman,et al.  A stochastic map for uncertain spatial relationships , 1988 .

[48]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[49]  Olivier D. Faugeras,et al.  Building, Registrating, and Fusing Noisy Visual Maps , 1988, Int. J. Robotics Res..

[50]  Kurt Konolige,et al.  Incremental mapping of large cyclic environments , 1999, Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375).

[51]  Ingemar J. Cox,et al.  Autonomous Robot Vehicles , 1990, Springer New York.

[52]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[53]  Lindsay Kleeman,et al.  Mobile Robot Sonar for Target Localization and Classification , 1995, Int. J. Robotics Res..

[54]  José A. Castellanos,et al.  Multisensor fusion for simultaneous localization and map building , 2001, IEEE Trans. Robotics Autom..

[55]  Eduardo Mario Nebot,et al.  Optimization of the simultaneous localization and map-building algorithm for real-time implementation , 2001, IEEE Trans. Robotics Autom..

[56]  Juan D. Tardós,et al.  Data association in stochastic mapping using the joint compatibility test , 2001, IEEE Trans. Robotics Autom..

[57]  Riccardo Poli,et al.  Robust mobile robot localisation from sparse and noisy proximity readings using Hough transform and probability grids , 2001, Robotics Auton. Syst..