Multi-Sensor SLAM with Online Self-Calibration and Change Detection

We present a solution for constant-time self-calibration and change detection of multiple sensor intrinsic and extrinsic calibration parameters without any prior knowledge of the initial system state or the need of a calibration target or special initialization sequence. This system is capable of continuously self-calibrating multiple sensors in an online setting, while seamlessly solving the online SLAM problem in real-time. We focus on the camera-IMU extrinsic calibration, essential for accurate long-term vision-aided inertial navigation. An initialization strategy and method for continuously estimating and detecting changes to the maximum likelihood camera-IMU transform are presented. A conditioning approach is used, avoiding problems associated with early linearization. Experimental data is presented to evaluate the proposed system and compare it with artifact-based offline calibration developed by our group.