Fiducial marker detection and pose estimation from LIDAR range data

Abstract

Light Detection and Ranging (LIDAR) systems are three dimensional (3D) imaging sensors applied for mapping terrain, measuring structural dimensions, and navigating robots. Pulsed laser rangefinders provide precise range measurements that require an estimate of sensor pose for transformation into world coordinates. Pose information is frequently provided with extrinsic sources such as Global Positioning System (GPS) or an Inertial Measurement Unit (IMU). Unreliable signal availability for GPS in military environments and the high cost of IMUs limit the employment of these extrinsic sources. Determining pose intrinsically by detecting landmarks in the environment within the sensor data is more ideal. Fiducial markers with known geometric dimensions and orientation provide a means of estimating LIDAR pose and registering data. Presented is a method for landmark detection and pose estimation within range data. Cylinder, cone, and sphere geometries are assessed for use as fiducial markers. The detection algorithm extracts geometric features from LIDAR point data and tests for fit to a fiducial marker model. Geometric feature extraction compresses the data set and leads to a potential intrinsic registration method using environment and marks. The detection accuracy and pose estimation precision are examined with terrestrial LIDAR range data captured in various outdoor street environments.