3D Point Cloud-Based Indoor Mobile Robot in 6-DoF Pose Localization Using a Wi-Fi-Aided Localization System

Six-DoF (Six-degree-of-freedom) pose localization based on 3D point clouds is a challenging task for LBSs (localization-based services). This paper proposes a robust and efficient method that uses multimodal information (vision and Wi-Fi signal information) to estimate the 6-DoF pose of an RGBD camera on a robot with respect to complex 3D textured models of the indoor environment that can contain more than 650,000,000 points. Our developed method narrows the search scope, which delimits boundaries initially using the Wi-Fi location system and applies an environment-adaptive approach to determine the radius of the search sphere based on the signal stability of the Wi-Fi location system. In addition, we propose an algorithm for estimating a novel correspondence between local points with a 3D submap by combining 3D points and surface normals to acquire absolute poses from noisy and outlier-contaminated matching point sets for RGBD sensors in dynamic indoor scenes. Then, a novel two-level spatial verification strategy is used to estimate an accurate pose, which includes the use of a RANSAC (Random Sample Consensus) algorithm for identification and a direct least-square method to acquire the pose from the inliers. The proposed method has been implemented and tested extensively in various indoor scenes. The experimental results demonstrate that the Wi-Fi-aided localization system can efficiently localize a mobile robot in a variety of large-scale 3D point cloud datasets to realize stable time consumption and similar performance to state-of-the-art methods.