Where am I? Localization techniques for Mobile Robots A Review

Autonomous navigation is one of the most challenging competencies required of a mobile robot. In order to accomplish successful navigation, a mobile robot must be competent in the four main elements of autonomous navigation: perception- the robot must be capable of interpreting its sensors to configure useful data about its environment; localization- the robot must be capable of determining its state within that environment; cognition- the robot must be make meaningful decisions on its actions in order to achieve its goals; and motion control- the robot must be capable of modulating its motor outputs to accurately achieve its desired trajectory. Of these four elements, localization has received the most attention by researchers in recent years, and as a result, we are seeing tremendous advances being made. This paper will provide an overview of the most commonly used localization techniques for mobile robots. We highlight the advantages and challenges associated with each technique and also investigate the various sensor fusion approaches that are being applied to enhance the overall accuracy and reliability of the localization system.

[1]  Fawzi Nashashibi,et al.  Improving poor GPS area localization for intelligent vehicles , 2017, 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI).

[2]  Vijay John,et al.  3D point cloud map based vehicle localization using stereo camera , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[3]  Akshya Swain,et al.  Reducing Low-Cost INS Error Accumulation in Distance Estimation Using Self-Resetting , 2014, IEEE Transactions on Instrumentation and Measurement.

[4]  Albert-Jan Baerveldt,et al.  Localization in changing environments - estimation of a covariance matrix for the IDC algorithm , 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).

[5]  Jing-Yu Yang,et al.  Monocular odometry in country roads based on phase-derived optical flow and 4-DOF ego-motion model , 2011, Ind. Robot.

[6]  Gerhard P. Hancke,et al.  Pose Estimation of a Mobile Robot Based on Fusion of IMU Data and Vision Data Using an Extended Kalman Filter , 2017, Sensors.

[7]  Abdelmoula Bekkali,et al.  RFID Indoor Positioning Based on Probabilistic RFID Map and Kalman Filtering , 2007, Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007).

[8]  Paul Newman,et al.  FARLAP: Fast robust localisation using appearance priors , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Paul Newman,et al.  Work smart, not hard: Recalling relevant experiences for vast-scale but time-constrained localisation , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Seung-Hwan Choi,et al.  Enhanced outdoor localization of multi-GPS/INS fusion system using Mahalanobis Distance , 2013, 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[11]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[12]  Jurek Z. Sasiadek,et al.  Sensor Fusion for Dead-Reckoning Mobile Robot Navigation , 2001 .

[13]  Ignacio Parra,et al.  Accurate Global Localization Using Visual Odometry and Digital Maps on Urban Environments , 2012, IEEE Transactions on Intelligent Transportation Systems.

[14]  Luigi Palopoli,et al.  Indoor Localization of Mobile Robots Through QR Code Detection and Dead Reckoning Data Fusion , 2017, IEEE/ASME Transactions on Mechatronics.

[15]  Gordon Wyeth,et al.  FAB-MAP + RatSLAM: Appearance-based SLAM for multiple times of day , 2010, 2010 IEEE International Conference on Robotics and Automation.

[16]  Henning Lategahn,et al.  How to learn an illumination robust image feature for place recognition , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[17]  Ming Yang,et al.  Ground-Texture-Based Localization for Intelligent Vehicles , 2009, IEEE Transactions on Intelligent Transportation Systems.

[18]  Martin Vossiek,et al.  Multi-modal sensor fusion for indoor mobile robot pose estimation , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[19]  Achim J. Lilienthal,et al.  SIFT, SURF and Seasons: Long-term Outdoor Localization Using Local Features , 2007, EMCR.

[20]  Yunhua Li,et al.  Localization of leader-follower formations using kinect and RTK-GPS , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[21]  Joachim Hertzberg,et al.  Evaluation of 3D registration reliability and speed - A comparison of ICP and NDT , 2009, 2009 IEEE International Conference on Robotics and Automation.

[22]  Danwei Wang,et al.  GPS-Based Tracking Control for a Car-Like Wheeled Mobile Robot With Skidding and Slipping , 2008, IEEE/ASME Transactions on Mechatronics.

[23]  Wolfram Burgard,et al.  Monte Carlo localization for mobile robots , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[24]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[25]  Javier González,et al.  Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM , 2008, IEEE Transactions on Robotics.

[26]  Wolfram Burgard,et al.  An experimental comparison of localization methods , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[27]  Hiroshi Murase,et al.  Single camera vehicle localization using SURF scale and dynamic time warping , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[28]  Sebastien Glaser,et al.  Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving , 2017, IEEE Transactions on Intelligent Vehicles.

[29]  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).

[30]  Jaehwan Kim,et al.  UKF data fusion of odometry and magnetic sensor for a precise indoor localization system of an autonomous vehicle , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[31]  Jari Saarinen,et al.  Normal distributions transform Monte-Carlo localization (NDT-MCL) , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  Li Renju.  Data registration in 3-D scanning systems , 2004 .

[33]  Ryan M. Eustice,et al.  Visual localization within LIDAR maps for automated urban driving , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[34]  F. Aghili,et al.  Driftless 3-D Attitude Determination and Positioning of Mobile Robots By Integration of IMU With Two RTK GPSs , 2013, IEEE/ASME Transactions on Mechatronics.

[35]  Wilfried Philips,et al.  Consistent ICP for the registration of sparse and inhomogeneous point clouds , 2016, 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE).

[36]  Othman Maklouf,et al.  Performance Evaluation of GPS \ INS Main Integration Approach , 2014 .

[37]  Ren C. Luo,et al.  Multisensor fusion and integration: approaches, applications, and future research directions , 2002 .

[38]  Seiichi Mita,et al.  Urban road localization by using multiple layer map matching and line segment matching , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[39]  KyuCheol Park,et al.  Dead Reckoning Navigation for Autonomous Mobile Robots , 1998 .

[40]  Yonghuai Liu,et al.  Improving ICP with easy implementation for free-form surface matching , 2004, Pattern Recognit..

[41]  Juan Andrade-Cetto,et al.  Localization in highly dynamic environments using dual-timescale NDT-MCL , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[42]  Giovanni Ulivi,et al.  An outdoor navigation system using GPS and inertial platform , 2001, 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Proceedings (Cat. No.01TH8556).

[43]  B. Matias,et al.  High-accuracy low-cost RTK-GPS for an unmannned surface vehicle , 2015, OCEANS 2015 - Genova.

[44]  Sebastian Thrun,et al.  Robust vehicle localization in urban environments using probabilistic maps , 2010, 2010 IEEE International Conference on Robotics and Automation.

[45]  Mohammad A. Jaradat,et al.  Multiple sensor fusion for mobile robot localization and navigation using the Extended Kalman Filter , 2015, 2015 10th International Symposium on Mechatronics and its Applications (ISMA).