Information Driven Self-Calibration for Lidar-Inertial Systems

Multi-modal estimation systems have the advantage of increased accuracy and robustness. To achieve accurate sensor fusion with these types of systems, a reliable extrinsic calibration between each sensor pair is critical. This paper presents a novel self-calibration framework for lidar-inertial systems. The key idea of this work is to use an informative path planner to find the admissible path that produces the most accurate calibration of such systems in an unknown environment within a given time budget. This is embedded into a simultaneous localization, mapping and calibration lidar-inertial system, which involves challenges in dealing with agile motions for excitation and large amount of data. Our approach has two stages: firstly, the environment is explored and mapped following a pre-defined path; secondly, the map is exploited to find a continuous and differentiable path that maximises the information gain within a sampling-based planner. We evaluate the proposed self-calibration method in a simulated environment and benchmark it with standard predefined paths to show its performance.