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Vehicles with prolonged autonomous missions have to maintain environmentawareness by simultaneous localization and mapping (SLAM). Closed loopcorrection is substituted by interpolation in rigid body transformation spacein order to systematically reduce the accumulated error over different scales.The computation is divided to an edge computed lightweight SLAM and iterativecorrections in the cloud environment. Tree locations in the forest environmentare sent via a potentially limited communication bandwidths. Data from a realforest site is used in the verification of the proposed algorithm. Thealgorithm adds new iterative closest point (ICP) cases to the initial SLAM andmeasures the resulting map quality by the mean of the root mean squared error(RMSE) of individual tree clusters. Adding 4% more match cases yields the meanRMSE 0.15 m on a large site with 180 m odometric distance.

Long-Term Autonomy in Forest Environment using Self-Corrective SLAM