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SLAM loopback detection method based on mobile robot

A technology of mobile robots and detection methods, which is applied in the directions of instruments, measuring devices, surveying and mapping, and navigation, and can solve problems such as difficulty in ensuring global consistency.

Pending Publication Date: 2021-04-16
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there are many open source laser SLAM overall solutions on the market, but most of them are difficult to guarantee global consistency

Method used

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  • SLAM loopback detection method based on mobile robot
  • SLAM loopback detection method based on mobile robot
  • SLAM loopback detection method based on mobile robot

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0068] Embodiment 1: laser pretreatment

[0069] S101. Using the pre-integrated value of the IMU to remove the laser motion distortion caused by the robot motion;

[0070] S102. A frame of laser point cloud data set is denoted as P={p 1 ,p 2 ,p 3 ,...,p n}, and projected onto the distance image;

[0071] S103, select a row of continuous points on the distance image, the set is denoted as R, point p i is the midpoint, p can be calculated by the following formula i The curvature value of :

[0072]

[0073] where d i for point p i Euclidean distance to lidar, set a value c b , when c is greater than c b is an edge point when c is less than c b is a plane point.

Embodiment 2

[0074] Example 2: Establishment of local subgraphs

[0075] S201. The relative attitude transformation of two consecutive frames, that is, the radar odometer is obtained by point-to-plane and point-to-edge scan matching methods, wherein the distance from the edge feature point to the line is calculated as follows:

[0076]

[0077] Point-to-surface calculations for planar features are as follows:

[0078]

[0079] Among them, and k+1 are continuous and associated two frames of laser light, i, j, l and m are the points in the two frames of laser light;

[0080] S202, using the LM nonlinear optimization method to obtain the pose transformation of two consecutive frames, that is, the radar odometer;

[0081] S203, select a key frame, set a distance value l and an angle value θ, when the distance of a certain frame is greater than l or the angle offset is greater than θ than the previous key frame, it will be selected as a key frame;

[0082] S204. The key frames form a lo...

Embodiment 3

[0083] Embodiment 3: Brand new descriptor, Edge-Planar Scan Context (EPSC) establishment

[0084] S301, extracting feature points, setting all point cloud sets of a local submap as S={s 1 ,s 2 ,...,s n}, and s k =(x k ,y k ,z k ), S e ={s e1 ,s e2 ,...,s em} and S p ={s p1 ,s p2 ,...,s p3} respectively represent the set of edge feature points and plane feature points in the sub-graph;

[0085] S302. Establish the sensor coordinate system, adopt the top view of the 3D scanned point cloud, and divide the ground area into multiple subspaces;

[0086] S303. The local subgraph is divided according to the azimuth and radial direction, and is divided into several rings, sectors, and subspaces with different colors, and each point s in the point cloud k can be expressed in polar coordinates:

[0087] the s k =[ρ k , θ k ,z k ]

[0088]

[0089]

[0090] Among them, the azimuth angle is specifically: from 0 to 2π in the lidar frame, and the radial direction i...

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Abstract

The invention belongs to the technical field of robot positioning, and mainly relates to an SLAM loopback detection method based on a mobile robot. The SLAM loopback detection method comprises the following steps of: S1, carrying out laser preprocessing on laser motion distortion generated by the motion of the robot, and extracting the edge point and plane point features in a distance image; S2, converting relative attitudes of continuous frames to obtain a radar odometer, and forming a local sub-graph through selection of key frames; and S3, adopting a brand-new descriptor EPSC to represent local sub-graph features to carry out loop-back detection. According to the SLAM loopback detection method, a brand-new descriptor EPSC is adopted for loop detection, drift errors can be effectively reduced, a global map with more optimized consistency is established, and the closed-loop detection precision, recall rate and calculation efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of robot positioning, and in particular relates to a mobile robot-based SLAM loop detection method. Background technique [0002] With the rapid development of computer science and technology, the research on autonomous navigation of wheeled mobile robots has become one of the research hotspots of intelligent vehicle transportation system, among which path planning is an important technology in the field of intelligent vehicle research. When a mobile robot performs various tasks, such as navigation, delivery, search and rescue, etc., it generally needs to construct a map of its working environment and determine its own position in the environment, which requires simultaneous localization and mapping (simultaneous localization and mapping, Hereinafter referred to as SLAM) technology. Among them, lidar-based SLAM technology has many advantages and has become one of the most concerned SLAM directions. At pres...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/20
Inventor 刘茛王庆志江涛苏晓杰黄江帅刁羽峰
Owner CHONGQING UNIV
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