An overview of mobile robot navigation technology

YAO, Chenpeng; SHI, Wenbo; LIU, Chengju; CHEN, Hong; CHEN, Qijun

doi:10.1360/ssi-2022-0420

Cited by 2 publications

(1 citation statement)

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“…Robots are a product of multidisciplinary integration, capable of semi-autonomous or fully autonomous operations. Autonomous mobile robots possess multiple functions including environmental perception, dynamic decision making, path planning, and autonomous navigation, allowing them to execute specific tasks in unknown environments [ 1 ]. The application of mobile robots in precision agriculture is increasingly widespread, with significant growth in the use of orchard robots.…”

Section: Introductionmentioning

confidence: 99%

LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments

Zhang,

Chen,

Xue

et al. 2024

Sensors

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To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.

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Section: Introductionmentioning

confidence: 99%