Adaptive localization for mobile robots in urban environments using low-cost sensors and enhanced topological map

This paper presents a technique for accurate localization of mobile robots using an enhanced topological map and using the low-cost sensors such as wheel odometer, global positioning system (GPS), and mono-camera. The localization framework is based on EKF to fuse the sensor data and the topological map. The sensor data include the positions of traffic marks measured by camera and topological map having the actual positions of traffic marks extracted from aerial or satellite images in advance. Our approach obtains the adaptive parameter for EKF localization by matching two positions, measured by camera and extracted from topological map, on each traffic mark. The adaptive parameter reflects the geographical characteristics, e.g. hill, corner, and road surfaces. The proposed method has shown high accuracy result and apparently better performance of the EKF localization with adaptive parameter. The proposed method is economically feasible and practically applicable to commercial robots using the low-cost sensors and providing the reliable localization services.