Factors to Evaluate Capability of Map for Vehicle Localization

Abstract

Recently, autonomous vehicle’s self-localization based on the matching of laser scanner data to the high definition (HD) map become more popular due to the availability of HD map and price down of light detection and ranging technologies. Many types of research have been done to achieve locally and globally accurate HD map for accurate localization. However, the global accuracy of the map does not guarantee accurate self-localization within the map. To achieve accurate self-localization, the map should satisfy some requirements. In this paper, the focus is made on the map, as one of the high potential sources of error in localization. By investigating the erroneous scenarios in the map and comparing their characteristics, we introduced four criteria for the self-localization ability of the map. These criteria are feature sufficiency, layout, local similarity, and representation quality of the map. Then, in order to quantify these criteria, we introduce several factors for each criterion. Unlike evaluation criteria which are defined regardless of the map formats, factors are defined based on normal distribution map which is a map format of normal distribution transformation scan-matching. These factors are calculated for each position in the map, based on the map features within its local vicinity. By conducting the experiments in Shinjuku, Japan, we have evaluated these factors in a different part of the map with different scenarios by comparing them with the self-localization error.

Keywords

• Autonomous vehicles self-localization
• scan matching
• normal distribution transform
• map evaluation
• high definition map