Navigation and Mapping in Forest Environment Using Sparse Point Clouds

Paavo Nevalainen,Tomi Westerlund,Jukka Heikkonen,Timo Melkas,Kirsi Riekki,Qingqing Li

doi:10.3390/rs12244088

Paavo Nevalainen, Tomi Westerlund + Show 4 more

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https://doi.org/10.3390/rs12244088

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Journal: Remote Sensing	Publication Date: Dec 14, 2020
Citations: 11	License type: CC BY 4.0

Affiliation: University of Turku, Metsähallitus (Finland)

Abstract

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.

Highlights

Modern Cut-to-length (CTL) harvesters [1] cut, delimb and buck the stem to the different timber assortments at the work site
A tree map is a collection of geographic tree locations and stem properties, such as the diameter at breast height (DBH), branch height, and stem profile, which can serve as a bridge between on-ground mapping [4] and aerial mapping [5], as well as providing a possible basis for future autonomous or semi-automatic logging operations
This detail shows a systematic difference between sparse SLAM (sSLAM) and LLOAM: Trees detected by sSLAM

Summary

Introduction

Modern Cut-to-length (CTL) harvesters [1] cut, delimb and buck the stem to the different timber assortments at the work site. Local tree maps are needed for assisted local movement planning In this field, two co-ordinate systems are mainly used: Global Navigation Satellite System (GNSS) and the local co-ordinates defined by the tree trunks. Two co-ordinate systems are mainly used: Global Navigation Satellite System (GNSS) and the local co-ordinates defined by the tree trunks These are fused, whenever possible, and there are only two extreme cases where this is difficult: (1) Final cutting in an environment with a monotonic ground profile, where only GNSS can be used by the forwarder; and (2) almost complete canopy cover in a highly rugged environment, where GNSS information becomes erroneous.

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