LiDAR-only based navigation algorithm for an autonomous agricultural robot

•New method to extract lines from a point set.•Improvement regards to PEARL and RANSAC algorithms.•Application to an autonomous agricultural robot.•Proposal of a navigation algorithm based on the line detection. The purpose of the work presented in this paper is to develop a general and robust appro...

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Bibliographic Details
Published inComputers and electronics in agriculture Vol. 154; pp. 71 - 79
Main Authors Malavazi, Flavio B.P., Guyonneau, Remy, Fasquel, Jean-Baptiste, Lagrange, Sebastien, Mercier, Franck
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.11.2018
Elsevier BV
Elsevier
Subjects
Algorithms
Automatic
Autonomous navigation
Computer simulation
Constraint modelling
Crop navigation
Crops
Data analysis
Engineering Sciences
Lidar
LiDAR measurements
Line extraction
Navigation systems
Outliers (statistics)
PEARL
RANSAC
Robots
Surveying
Three dimensional models
RANSAC
Crop navigation
PEARL
Line extraction
LiDAR measurements
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Summary:•New method to extract lines from a point set.•Improvement regards to PEARL and RANSAC algorithms.•Application to an autonomous agricultural robot.•Proposal of a navigation algorithm based on the line detection. The purpose of the work presented in this paper is to develop a general and robust approach for autonomous robot navigation inside a crop using LiDAR (Light Detection And Ranging) data. To be as robust as possible, the robot navigation must not need any prior information about the crop (such as the size and width of the rows). The developed approach is based on line extractions from 2D point clouds using a PEARL based method. In this paper, additional filters and refinements of the PEARL algorithm are presented in the context of crop detection. A penalization of outliers, a model elimination step, a new model search and a geometric constraint are proposed to improve the crop detection. The approach has been tested over a simulator and compared with classical PEARL and RANSAC based approaches. It appears that adding those modification improved the crop detection and thus the robot navigation. Those results are presented and discussed in this paper. It can be noticed that even if this paper presents simulated results (to ease the comparison with other algorithms), the approach also has been successfully tested using an actual Oz weeding robot, developed by the French company Naio Technologies.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2018.08.034