Latest revision as of 20:57, 3 February 2021

Abstract

Many urban navigation applications (e.g., autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy. Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings. We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments. Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps. Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap. By reducing the final map to the flat road surface, imprints of other vehicles are removed. The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution. To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map. As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude. Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10centimeter range. Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic. The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads.

Original document

The different versions of the original document can be found in:

• http://dx.doi.org/10.15607/rss.2007.iii.016

• https://doi.org/10.15607/rss.2007.iii.016

• http://www.roboticsproceedings.org/rss03/p16.pdf,

http://driving.stanford.edu/papers/RSS2007.pdf,

http://www.roboticsproceedings.org/rss03/p16.html,

https://dblp.uni-trier.de/db/conf/rss/rss2007.html#LevinsonMT07,

https://academic.microsoft.com/#/detail/179122090