A Robust LiDAR-Inertial Multi Constraint-Based Localization for Agricultural Environments

Accurate state estimation is essential for an autonomous agricultural robot's reliable operations. The effectiveness of state estimation is influenced by a number of factors, such as sensor-fusion algorithms, the environment, and sensor quality. When the robot traverses in large-scale scenarios, the distance travelled and high-speed mobility produce a drift in the estimation process and should be carefully considered. Moreover, the time-varying noise in sensors affects odometry accuracy further; this is especially noticeable in long travel. This research work is related to the multi-constraints-based state estimation in large unstructured environments with uneven terrain, with a focus on agricultural applications. Using LiDAR-IMU based fusion, our goal is to provide a reliable & accurate localization solution in complex environments like agricultural fields. Furthermore, the agricultural environments become more challenging due to the uneven terrain and lack of features. Our research proposes a hybrid framework which combines factor graph-based optimization & adaptive Kalman filtering to address these challenges in complex environments. Furthermore, performance evaluation is conducted on self-collected datasets from agricultural environments as well as on open-access datasets such as GRACO & KITTI.