Localization using automotive laser scanners and local pattern matching

Autonomous driving requires vehicle positioning with accuracies of a few decimeters. Typical low-cost GNSS sensors, as they are commonly used for navigation systems, are limited to an accuracy of several meters. Also, they are restricted in reliability because of outages and multipath effects. To improve accuracy and reliability, 3D features can be used, such as pole-like objects and planes, measured by a laser scanner. These features have to be matched to the reference data, given by a landmark map. If we use a nearest neighbor approach to match the data, we will likely get wrong matches, especially at positions with a low initial accuracy. To reduce the number of wrong matches, we use feature patterns. These patterns describe the spatial relationship of a specific number of features and are determined for every possible feature combination, separated in reference and online features. Given these patterns, the correspondences of the measured features can be determined by finding the corresponding patterns in the reference data. We acquired reference data by a high precision Mobile Mapping System. In an area of 2.8 km² we automatically extracted 1390 pole-like objects and 2006 building facades. A (second) vehicle equipped with an automotive laser scanner was used to generate features with lower accuracy and reliability. In every scan of the laser scanner we extracted landmarks (poles and planes) online. We then used our proposed feature matching to find correspondences. In this paper, we show the performance of the approach for different parameter settings and compare it to the nearest neighbor matching commonly used. Our experimental results show that, by using feature patterns, the rate of false matches can be reduced from about 80 % down to 20 %, compared to a nearest neighbor approach.