Navigation and Mapping in Forest Environment Using Sparse Point Clouds




Nevalainen P., Li Q., Melkas T., Riekki K., Westerlund T., Heikkonen, J.

PublisherMDPI

Switzerland

2020

Remote Sensing

4088

12

24

19

2072-4292

DOIhttps://doi.org/10.3390/rs12244088

https://www.mdpi.com/2072-4292/12/24/4088

https://research.utu.fi/converis/portal/detail/Publication/50545250



Odometry during forest operations is demanding, involving limited field of vision (FOV), back-and-forth work cycle movements, and occasional close obstacles, which create problems for state-of-the-art systems. We propose a two-phase on-board process, where tree stem registration produces a sparse point cloud (PC) which is then used for simultaneous location and mapping (SLAM). A field test was carried out using a harvester with a laser scanner and a global navigation satellite system (GNSS) performing forest thinning over a 520 m strip route. Two SLAM methods are used: The proposed sparse SLAM (sSLAM) and a standard method, LeGO-LOAM (LLOAM). A generic SLAM post-processing method is presented, which improves the odometric accuracy with a small additional processing cost. The sSLAM method uses only tree stem centers, reducing the allocated memory to approximately 1% of the total PC size. Odometry and mapping comparisons between sSLAM and LLOAM are presented. Both methods show 85% agreement in registration within 15 m of the strip road and odometric accuracy of 0.5 m per 100 m. Accuracy is evaluated by comparing the harvester location derived through odometry to locations collected by a GNSS receiver mounted on the harvester.


Last updated on 2024-26-11 at 11:41