Loop Closure Detection in Complex Orchards Based on Density Binary Pattern
Abstract
In order to reduce the cumulative drift error of the orchard robot in simultaneous localization and mapping(SLAM), a loop closure detection algorithm was proposed based on density binary pattern(DBP). The LiDAR scanning was divided into eight-bit binary bins along the vertical height direction. If the number of point clouds in the bin exceeded five, it was considered a valid scan, and the bin value was set to be 1, otherwise 0. Further, the eight-bit data were projected to construct the DBP descriptor. The DBP descriptor contained point cloud density and height information and had a significant distinguishing effect on tall fruit trees and low shrubs. A two-stage search algorithm was utilized to ensure the task real-time requirements in the large-scale orchard. Firstly, to extract a low-dimensional ring factor vector of DBP, the K-nearest neighbor candidate loop closure frames were quickly found in the K-dimensional tree(KD-Tree), which was constructed by the ring factors. The maximum similarity between the candidates and the query frame was obtained. If the distance threshold condition was met, the candidate frame was considered an effective target loop closure. The experiment was carried out in three orchards of different scales. In the orchard scene with multiple loop closure events, the root mean square error and standard deviation of the DBP-LeGO-LOAM trajectory were 0.24m and 0.09m, compared with the LeGO-LOAM algorithm which had been reduced 81% and 91% respectively. It provided an effective solution for improving the mapping and localization accuracy of orchard robots.
Keywords: complex orchards, loop closure detection, density binary pattern, SLAM
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KIM G, KIM A. Scan context: egocentric spatial descriptor for place recognition within ЗD point cloud map[C] //2018 IEEE/ RSJ International Conference on Intelligent Robots and Systems (IROS) ,2018:4802 -4809.
SAEEDI S, TRENTINI M, SETO M, et al. Multiple-robot simultaneous localization and mapping: a review[ J ]. Journal of Field Robotics,2016,33( 1) ;3 — 46.
LI Kailin, LI Jiansheng, WANG Ancheng. An overview of GNSS/INS/visual integrated navigation technology [J]. Journal of Navigation and Positioning, 2023 ,ll(l);9-l5. (in Chinese)
FENG Yi,TU Rui, HAN Junqiang, et al. Study on a tight integrated navigation and positioning algorithm of GNSS/visual observation [ J ]. GNSS World of China,2021 ,46(6) ;49 -54. (in Chinese)
SONG Z,ZHOU Z,WANG W,et al. Canopy segmentation and wire reconstruction for kiwifruit robotic harvesting[ J ]. Computers and Electronics in Agriculture,2021 ,181 ; 105933.
AGUIAR A, DOS SANTOS F, CUNHA J, et al. Localization and mapping for robots in agriculture and forestry: a survey[J]. Robotics,2020,9(4) :97.
MARTfNEZ-CASASNOVAS J, RUFAT J,ARN(5 J,et al. Mobile terrestrial laser scanner applications in precision fruticulture/ horticulture and tools to extract information from canopy point clouds [ J]. Precision Agriculture ,2017,18 ( 1 ) : 1 1 1 - 132.
LI Yanzhou, SHI Yifeng, TU Wei, et al. Research on autonomous navigation between rows of sugarcane in the whole growth cycle based on LiDAR [J]. Journal of Chinese Agricultural Mechanization ,2022 ,43 ( 3 ) : 153 — 158,177. (in Chinese)
MIAO Yanlong,PENG Cheng,GAO Yang,et al. Research actuality and prospect of picking robot for fruits and vegetables[ J ]. Transactions of the Chinese Society for Agricultural Machinery,2021 ,52(Supp. ) :43 -50. (in Chinese)
RUSU R, BLODOW N,BEETZ M. Fast point feature histograms ( FPFH ) for 3D registration [С ] // 2009 IEEE International Conference on Robotics and Automation ,2009 ;3212 -3217.
TOMBARI F, SALTI S, DI STEFANO L. Unique shape context for 3D data description [ С ] //Proceedings of the ACM Workshop on ЗD Object Retrieval ,2010:57 -62.
WANG II, WANG C, XIE L. Intensity scan context: coding intensity and geometry relations for loop closure detection[ С] // 2020 IEEE International Conference on Robotics and Automation (ICRA) ,2020:2095 -2101.
W ANG Y, SUN Z, XU С Z, et al. LiDAR iris for loop-closure detection [ С ] //2020 IEEE/RSJ International Conference on Intelligent Robots and Systems ( IROS) ,2020:5769 -5775.
BARSAN I A, WANG S, POKROVSKY A,et al. Learning to localize using a LiDar intensity map [ J ]. arXiv preprint arXiv; 201210902,2020.
YIN H, TANG L, DING X, et al. Locnet; global localization in 3D point clouds for mobile vehicles [ С ] //2018 IEEE Intelligent Vehicles Symposium (IV) ,2018;728 -733.
QI CR, SU II, MO K, et al. Pointnet: deep learning on point sets for 3D classification and segmentation [ С ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition ,2017 :652 -660.
QI С R , YI L, SU H, et al. Pointnet + + : deep hierarchical feature learning on point sets in a metric space [С ] // 31st Conference on Neural Information Processing Systems (NIPS 2017) ,Long Beach, CA, USA,2017.
GALVEZ-L6PEZ D, TARDOS J. Bags of binary words for fast place recognition in image sequences [ J ]. IEEE Transactions on Robotics,2012,28(5) : 1188 - 1197.
HE L, FANG W, ZHAO G, et al. Fruit yield prediction and estimation in orchards; a state-of-the-art comprehensive review for both direct and indirect methods [ J ]. Computers and Electronics in Agriculture,2022 ,195 :106812.
SONG Jian, ZHANG Tiezhong, XU Liming, et al. Research actuality and prospect of picking robot for fruits and vegetables [J] . Transactions of the Chinese Society for Agricultural Machinery ,2006 ,37 ( 5 ) : 15 8 — 162. (in Chinese)
LIU Chengliang, GONG Liang, YUAN Jin, et al. Current status and development trends of agricultural robots [J]. Transactions of the Chinese Society for Agricultural Machinery ,2022 ,53 (7) :1 -22,55. (in Chinese)
XU X, YIN H, CHEN Z, et al. Disco; differentiable scan context with orientation[ J] . IEEE Robotics and Automation Letters, 2021 ,6(2) :2791 -2798.
SHAN T,ENGLOT B. Lego-loam: lightweight and ground-optimized lidar odometry and mapping on variable terrain [С] // 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ,2018:4758 -4765.
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