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.
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