Global Localization Based on 3D Planar Surface Segments
This work addresses localization for mobile robots in 3D environments, but it is incremental as it builds on existing methods like planar segmentation and Kalman filtering.
The paper tackles global localization for mobile robots by matching planar surface segments from depth images to a topological map, achieving reliable and accurate pose estimation as validated experimentally with a Kinect sensor.
Global localization of a mobile robot using planar surface segments extracted from depth images is considered. The robot's environment is represented by a topological map consisting of local models, each representing a particular location modeled by a set of planar surface segments. The discussed localization approach segments a depth image acquired by a 3D camera into planar surface segments which are then matched to model surface segments. The robot pose is estimated by the Extended Kalman Filter using surface segment pairs as measurements. The reliability and accuracy of the considered approach are experimentally evaluated using a mobile robot equipped by a Microsoft Kinect sensor.