Map-matching based on light detection and ranging (LiDAR) is a promising method for accurate self-localization and recently has gained a wider focus due to the availability of high definition (HD) maps and price-down of LiDARs. In this method, the input scan of the LiDAR is matched to the prebuilt map to get a centimeter-level accuracy position of the vehicle. However, in some places of the map, due to the lack of features, the presence of the repetitive features, the layout of the features, and other factors, the map-matching error might exceed the required bound for autonomous driving. In our previous work, four criteria for evaluation of the features of the map was introduced and it is shown that by examining the corresponding factors for each criterion, the map-matching error can be modeled. In this work, one of the map criteria called local similarity is further investigated and in order to quantify the fulfillment of this criterion, three new factors, namely pfh_similarity, pfh_entropy, and battacharya_similarity are introduced. In addition to this, a framework for pre-estimation of the map-matching error considering these four criteria based on random forest regression is proposed. To evaluate the accuracy of the framework, experiments were conducted for 3.6 km in Shinjuku, Tokyo. Experimental results show that using the proposed framework, in 64.1% of the cases, the localization error can be estimated with less than 2.5cm of the estimation error.
Pre-Estimating Self-Localization Error of NDT-Based Map-Matching From Map Only
IEEE Transactions on Intelligent Transportation Systems ; 22 , 12 ; 7652-7666
2021-12-01
4536961 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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