Autonomous robots are highly complex systems. In order to operate in dynamic environments, adaptability in their decision-making algorithms is a must. Thus, the internal and external information that robots obtain from sensors is critical to re-evaluate their decisions in real time. Accuracy is key in this endeavor, both from the hardware side and the modeling point of view. In order to guarantee the highest performance, sensors need to be correctly calibrated. To this end, some parameters are tuned so that the particular realization of a sensor best matches a generalized mathematical model. This step grows in complexity with the integration of multiple sensors, which is generally a requirement in order to cope with the dynamic nature of real world applications. This project aims to deal with the calibration of an inertial measurement unit, or IMU, and a Light Detection and Ranging device, or LiDAR. An offline batch optimization procedure is proposed to optimally estimate the intrinsic and extrinsic parameters of the model. Then, an online state estimation module that makes use of the aforementioned parameters and the fusion of LiDAR-inertial data for local navigation is proposed. Additionally, it incorporates real time corrections to account for the time-varying nature of the model, essential to deal with exposure
to continued operation and wear and tear. Keywords: sensor fusion, multi-sensor calibration, factor graphs, batch optimization, Gaussian Processes, state estimation, LiDAR-inertial odometry, Error State Kalman Filter, Normal Distributions Transform.
