Research on navigation and mapping in pregnancy pig house based on laser-vision fusion technology

doi:10.3969/j.issn.1004-1524.20231148

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (10): 2358-2367.DOI: 10.3969/j.issn.1004-1524.20231148

• Biosystems Engineering • Previous Articles Next Articles

Research on navigation and mapping in pregnancy pig house based on laser-vision fusion technology

PAN Zibo¹^,²(), ZHOU Xin², XU Xing², LIU Kaige¹^,², JI Honghu³, LU Fuzeng², YE Chunlin¹^,^*(), ZHOU Weidong²^,^*()

1. School of Biological & Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
2. Institute of Animal Husbandry and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
3. Zhejiang Jinhua Academy of Agricultural Sciences, Jinhua 321017, Zhejiang, China

Received:2023-09-26 Online:2024-10-25 Published:2024-10-30

Abstract

Abstract:

Research has been conducted to address the problem of insufficient accuracy of the laser-based or vision-based navigation technologies in the complex facility environment of large-scale pig farms. In the present study, a new method was established by calibrating the intrinsic parameters of depth cameras, fusing data from laser radar and depth cameras for front-end odometry, and comprehensive optimization for back-end processing, loop closure detection, and mapping. A comparative study was conducted to evaluate the effectiveness of constructing 2D grid maps and point cloud maps by using laser-vision fusion mapping technology for passages and corridors. The results demonstrated that the fused 2D grid map provided more information than the single laser radar mapping, and it also displayed an intuitive point cloud representation, which could effectively present the road and color information from the actual scene. The relative error ranges for the coornidates of the starting potint and the terminal point were 0.02%-0.33% and 0.48%-1.62%, respectively, which were lower than those of the three algorithms used for laser mapping, indicating good accuracy and mapping efficiency.

Key words: lidar, depth camera, simultaneous localization and mapping, fusion navigation mapping, pig house

CLC Number:

S818.9

PAN Zibo, ZHOU Xin, XU Xing, LIU Kaige, JI Honghu, LU Fuzeng, YE Chunlin, ZHOU Weidong. Research on navigation and mapping in pregnancy pig house based on laser-vision fusion technology[J]. Acta Agriculturae Zhejiangensis, 2024, 36(10): 2358-2367.

Figures/Tables 17

Fig.1 Diagram of experimental device control system framework SLAM, Simultaneous localization and mapping; DC, Direct current.

Fig.2 Photos of STM32 F407 microcontroller (left) and Jetson nano development board (right)

Fig.3 Photos of Riegl M10P laser scanner (left) and RealSense L515 depth camera (right)

Fig.4 The front view (left) and rear view (right) of the experimental apparatus

Fig.5 Framework of laser-vision fusion technology

Fig.6 Photo of calibration board

Fig.7 Physical diagram (left) and its feature point extraction diagram (right) of limit switch

Fig.8 Diagram of reprojection error

Fig.9 Flow char of classification based on k-means algorithm

Fig.10 Photo of construction experiment site

Fig.11 The two-dimensional raster map of corridor (up) and passage (down) of pregnancy house based on Gmapping algorithm

Fig.12 The two-dimensional raster map of corridor (up) and passage (down) of pregnancy house based on Cartographer algorithm

Fig.13 The two-dimensional raster map of corridor (up) and passage (down) of pregnancy house based on Katro algorithm

Table 1 Comparison of mapping effect of different algorithms

建图算法 Mapping algorithm	建图时间 Mapping time/s	平均偏移角度 Mean deviation angle/(°)	局部建图范围 Local mapping scope	地图完整度 Map integrity
Gmapping	630	10	大Wide	较为完整Relatively complete
Cartographer	803	5	小Small	不太完整Less complete
Katro	686	2	中Medium	较为完整Relatively complete
融合方法Fusion method	615	3	大Wide	十分完整Quite complete

Fig.14 2D grid map (A) and point cloud map (B) of corridor The green line represents the motion trajectory of the mobile robot. The same as below.

Fig.15 2D grid map (A) and point cloud map (B) of passage

Table 2 Coordinates of different mapping algorithms and relative errors

建图算法 Mapping algorithm	地点 Site	位置 Location	坐标 Coordinate(x,y)/m	相对误差 Relative error/%
Gmapping	走廊Corridor	起始Starting point	(0.15,-4.00)	0.37
		终点Terminal point	(60.00, 17.00)	2.03
	舍内Hoggery	起始Starting point	(0.20,-4.00)	0.98
		终点Terminal point	(45.00,-5.00)	0.96
Cartographer	走廊Corridor	起始Starting point	(0.30,-5.00)	0.40
		终点Terminal point	(64.00,-11.00)	0.51
	舍内Hoggery	起始Starting point	(-0.30, 6.00)	1.03
		终点Terminal point	(45.00,-6.00)	0.48
Katro	走廊Corridor	起始Starting point	(0.18,-4.00)	0.35
		终点Terminal point	(66.00,-2.00)	1.76
	舍内Hoggery	起始Starting point	(0.20,-5.00)	0.02
		终点Terminal point	(46.00,-6.00)	0.96
融合建图Fusion mapping	走廊Corridor	起始Starting point	(0.20,-5.00)	0.33
		终点Terminal point	(65.00,-6.00)	1.62
	舍内Hoggery	起始Starting point	(0.20,-5.00)	0.02
		终点Terminal point	(45.00,-6.00)	0.48

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Research on navigation and mapping in pregnancy pig house based on laser-vision fusion technology

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