Leveraging the OSM building data to enhance the localization of an urban vehicle

In this paper we present a technique that takes advantage of detected building façades and OpenStreetMaps data to improve the localization of an autonomous vehicle driving in an urban scenario. The proposed approach leverages images from a stereo rig mounted on the vehicle to produce a mathematical representation of the buildings' façades within the field of view. This representation is matched against the outlines of the surrounding buildings as they are available on OpenStreetMaps. The information is then fed into our probabilistic framework, called Road Layout Estimation, in order to produce an accurate lane-level localization of the vehicle. The experiments conducted on the well-known KITTI datasets prove the effectiveness of our approach.

[1]  Bernardo Wagner,et al.  Autonomous robot navigation based on OpenStreetMap geodata , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[2]  Bastian Leibe,et al.  Joint 2D-3D temporally consistent semantic segmentation of street scenes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Nassir Navab,et al.  Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Michel Dhome,et al.  Vision-Based Differential GPS: Improving VSLAM / GPS Fusion in Urban Environment with 3D Building Models , 2014, 2014 2nd International Conference on 3D Vision.

[6]  Theo Gevers,et al.  3D Scene priors for road detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Philip David,et al.  Building facade detection, segmentation, and parameter estimation for mobile robot localization and guidance , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Jason J. Corso.  Discriminative modeling by Boosting on Multilevel Aggregates , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  D. Fox,et al.  Classification and Semantic Mapping of Urban Environments , 2011, Int. J. Robotics Res..

[10]  Alexei A. Efros,et al.  Recovering Surface Layout from an Image , 2007, International Journal of Computer Vision.

[11]  Upamanyu Madhow,et al.  Bayesian localization and mapping using GNSS SNR measurements , 2014, 2014 IEEE/ION Position, Location and Navigation Symposium - PLANS 2014.

[12]  Vincent Frémont,et al.  Mapping and localization using GPS, lane markings and proprioceptive sensors , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Bastian Leibe,et al.  OpenStreetSLAM: Global vehicle localization using OpenStreetMaps , 2013, 2013 IEEE International Conference on Robotics and Automation.

[14]  David Fernández Llorca,et al.  A Comparative Analysis of Decision Trees Based Classifiers for Road Detection in Urban Environments , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[15]  Augusto Luis Ballardini,et al.  A Framework for Outdoor Urban Environment Estimation , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[16]  Theo Gevers,et al.  Combining Priors, Appearance, and Context for Road Detection , 2014, IEEE Transactions on Intelligent Transportation Systems.

[17]  David Fernández Llorca,et al.  Curvature-based curb detection method in urban environments using stereo and laser , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

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

[19]  Konstantinos G. Derpanis,et al.  Overview of the RANSAC Algorithm , 2005 .

[20]  Mohamed Essayed Bouzouraa,et al.  In-vehicle Roundabout Perception Supported by A Priori Map Data , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[21]  Wolfram Burgard,et al.  Localization on OpenStreetMap data using a 3D laser scanner , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Li-Ta Hsu,et al.  GNSS/Onboard Inertial Sensor Integration With the Aid of 3-D Building Map for Lane-Level Vehicle Self-Localization in Urban Canyon , 2016, IEEE Transactions on Vehicular Technology.

[23]  Shoudong Huang,et al.  Towards robust vision-based self-localization of vehicles in dense urban environments , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[24]  Britta Hummel,et al.  Scene Understanding of Urban Road Intersections with Description Logic , 2008, Logic and Probability for Scene Interpretation.

[25]  Markus Schreiber,et al.  LaneLoc: Lane marking based localization using highly accurate maps , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[26]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[27]  Scott M. Sawyer,et al.  Geo-registering 3D point clouds to 2D maps with scan matching and the Hough Transform , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[28]  Philip David,et al.  Detecting Planar Surfaces in Outdoor Urban Environments , 2008 .

[29]  Marc Pollefeys,et al.  Leveraging 3D City Models for Rotation Invariant Place-of-Interest Recognition , 2011, International Journal of Computer Vision.

[30]  Ali Shahrokni,et al.  Urban 3D semantic modelling using stereo vision , 2013, 2013 IEEE International Conference on Robotics and Automation.

[31]  Yoshua Bengio,et al.  ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks , 2015, ArXiv.

[32]  Bruno Vallet,et al.  Generation of an integrated 3D city model with visual landmarks for autonomous navigation in dense urban areas , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[33]  Luc Van Gool,et al.  3D Urban Scene Modeling Integrating Recognition and Reconstruction , 2008, International Journal of Computer Vision.

[34]  Danfei Xu,et al.  Topometric localization on a road network , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[35]  Daniel G. Aliaga,et al.  A Survey of Urban Reconstruction , 2013, Comput. Graph. Forum.