Vehicle self-Iocalization based on the matching of Light detection and ranging (LiDAR) scans to the normal distribution (ND) map become more popular in recent years due to the price down and miniaturization of the LiDARs. In such methods, the source of self-Iocalization error can be divided into input scan quality, matching algorithm and map. In this work, we focus on the map, as one ofthe high potential sources of error. By investigating the erroneous scenarios in the map and comparing their characteristics, we come up with some criteria and requirements for the map to be able to perform self-Iocalization with a needed error. In this work, we propose four factors for quantified evaluation ofthe map requirements. These factors are feature count factor, layout factor, normal entropy factor, and local similarity factor ofthe map. We evaluated these four factors in a different part ofthe map with different scenarios by comparing them with the self-Iocalization error. Experimental results show that the local similarity factor with 0.59 of correlation with the maximum error has the highest contribution to the Iocalization error. For normal entropy factor, feature count factor, layout factor, correlations are 0.42, 0.36, and 0.34 respectively. By applying these four factors, maximum Iocalization error can be modeled with RMSE and R-squared (R2) of 0.44 and 0.598 respectively. Result of this study can be applied to the dynamic determination of the abstraction ratio of the map and sensor fusion as well.
Evaluation of Digital Map Ability for Vehicle Self-Iocalization
2018-06-01
2258364 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
EVALUATION OF DIGITAL MAP ABILITY FOR VEHICLE SELF-IOCALIZATION
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