A systematic analysis of scan matching techniques for localization in dense orchards

Abstract

In recent years, different methods have been studied to determine machinery position within a grove, as an alternative for complementing GNSS (global navigation satellite system) information in cases where GNSS signal is occluded. Such a situation can be observed when agricultural machinery travels under dense foliage or on the slopes of mountains. Scan matching techniques arise as a possible solution for localizing the machinery, complementing the absence of the GNSS signal when necessary. However, since key points are difficult to obtain in heterogeneous, unstructured and non-rigid environments (such as orchard plants), the performance of scan matching techniques often decreases in agricultural environments. This paper suggests dividing the point clouds into horizontal and vertical segments to improve the performance of scan-matching methods in orchards. It also examines the best way for registered frames to overlap. We validate the analysis with extensive experimentation in a Fuji apple orchard. The results show that the cumulative localization error in scan matching techniques can be notoriously decreased with selective parts of the orchard, by up to 60%. The experimentation performed herein suggests that the proposed methodology can complement the GNSS navigation in a middle-long path.

Keywords

• Global positioning system
• Point cloud registration
• Mobile sensing
• Vehicle localization
• Phenotyping