Real-time autonomous navigation of an electric wheelchair in large-scale urban area with 3D map

Abstract

This paper presents the real-time autonomous navigation of an electric wheelchair in a large-scale urban area. Accurate self-pose localization and well-chosen motion control are crucial for application to urban areas, as electric wheelchairs move on paved roads in dynamic environments and travel along sidewalks at a brisk speed. Our system is equipped with a localization module based on a 3D map and a path planning module based on a navigation map. However, the large-scale 3D map causes a high memory load, and the embedded PC can not deal with the map data. In addition, the large-scale navigation map increases the computational cost of path planning, which causes delays in navigation. To achieve real-time navigation independent of map size, we propose a 6-DoF pose localization switching reference 3D map and a two-step path planning framework. We ran tests by using an electric wheelchair on a real street in Tokyo and found that the proposed navigation system achieved autonomous navigation for over 8.8 km in about 133 minutes. The experimental results showed that the memory load was kept constant and the path planning was performed at high frequency, regardless of the size of the map or the distance to the destination.