RESEARCH FOR ESTIMATING ERROR FACTORS AND ITS CORRECTION ON POINT CLOUD DATA WITH LIDAR SLAM IN URBAN SPACE

Abstract

 In Japan, mobile mapping systems (MMSs) are used for the maintenance of civil infrastructures, such as roads and rivers, and generation of high precision three-dimensional maps for autonomous driving. Using MMS is difficult for local governments because it is very expensive. Therefore, we developed a vehicle-mounted sensing unit with commercially available and inexpensive sensors, and we evaluated its practicality. We considered the self-position estimation of the sensing unit using SLAM (Simultaneous Localization And Mapping) because GNSS (Global Navigation Satellite System) could not provide a fixed solution in environments with viaducts or skyscrapers on both sides. However, when the measurement data includes vegetation, the estimation accuracy decreases. In this research, the error factors of self-position estimation accuracy are investigated and a method for correcting the errors by removing their factors is proposed. In the result, the method was verified in some experiments, we confirmed it was useful.