Spatial LiDAR odometry and mapping for complex agricultural environments - Spatial FieldLOAM

The challenge of autonomous driving in natural environments, without the use of GNSS devices is addressed. It utilises the readings from a multichannel LiDAR, supported by IMU, and enhances the capabilities of the FieldSLAM algorithm to establish an independent localisation and mapping system. This system is designed for performing specific tasks in predefined agricultural areas, employing incremental LOAM techniques. By comparing the outcomes of the novel Spatial FieldLOAM algorithm with the assistance of a precise Inertial Measurement Unit (IMU) and using the state-of-the-art RTK-GPS system as the ground truth, it is concluded that the Spatial FieldLOAM achieves an error rate of 5.5%, whereas the Xsens IMU yields an error rate of 5.7%. In terms of Euclidean distances to the final RTK GPS supported localisation on a 68.7 m test run, the error rates are 3.78 m and 3.92 m, respectively, or 0.0038 m per epoch for the Spatial FieldLOAM algorithm during non-vegetation season. The tests were also conducted during the vegetation season in a total length of 210 m, revealing a difference of 3.07 m distance between the final position calculated by the Spatial FieldLOAM and Xsens IMU. • Novel LOAM: low computational complexity and high robustness - useable in nature. • High Accuracy: 5.5% error when compared to (RTK) ground truth data. • Superior Performance: better then Xsens IMU localisation (5.5% vs. 5.7%). • Computational Feasibility: possible to run the method on embedded systems. • Versatility in Challenging Environments: suitable for sparse and dense vegetation.