SG-ISBP: Orchard Robots Localization and Mapping With Ground Optimization and Loop Closure Detection Integration

Abstract

Orchard robots’ tasks rely on highly accurate, real-time trajectory estimation and map-building. In this article, a novel tightly coupled light detection and ranging (LiDAR) inertial simultaneous localization and mapping (SLAM) system, SG-ISBP-SLAM, is proposed. The algorithm involves both ground optimization and loop closure detection. Aiming at the uneven orchard ground, this article designs a ground segmentation method from a global perspective. It takes the nearest neighbor seed plane as the baseline and iteratively grows a global plane based on principal component analysis (PCA). The LiDAR scan is divided into 3-D concentric zone representations to assign an appropriate density of cloud points among bins. Based on the partition strategy, the improved spatial binary pattern (ISBP) is encoded for lower time-consuming loop closure detection. To validate the performance of the proposed algorithm, qualitative and quantitative experiments have been conducted. Experimental results indicate that SG-ISBP-SLAM provides low-time consumption and reliable nonflat ground segmentation capabilities. Moreover, the loop module can efficiently correct robot localization trajectory drift.