Reliable Data Association for Feature-Based Vehicle Localization using Geometric Hashing Methods

Reliable data association represents a main challenge of feature-based vehicle localization and is the key to integrity of localization. Independent of the type of features used, incorrect associations between detected and mapped features will provide erroneous position estimates. Only if the uniqueness of a local environment is represented by the features that are stored in the map, the reliability of localization is enhanced. In this work, a new approach based on Geometric Hashing is introduced to the field of data association for feature-based vehicle localization. Without any information on a prior position, the proposed method allows to efficiently search large map regions for plausible feature associations. Therefore, odometry and GNSS-based inputs can be neglected, which reduces the risk of error propagation and enables safe localization. The approach is demonstrated on approximately 10min of data recorded in an urban scenario. Cylindrical objects without distinctive descriptors, which were extracted from LiDAR data, serve as localization features. Experimental results both demonstrate the feasibility as well as limitations of the approach.