Improvement and application of RGB-D vision SLAM algorithm

CHENG Yi; BAI Jia-qi; XIU Chun-bo

doi:10.3788/OPE.20172513.0160

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Improvement and application of RGB-D vision SLAM algorithm

更新时间：2020-08-13

- Improvement and application of RGB-D vision SLAM algorithm
- Optics and Precision Engineering Vol. 25, Issue 10s, Pages: 160-166(2017)
- 作者机构：
  
  1. 天津工业大学电气工程与自动化学院天津,300387
  2. 天津市电工电能新技术重点实验室天津,300387
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172513.0160
  CLC： TP391.41
- Received：28 April 2017，
  
  Revised：10 June 2017，
  
  Published：25 November 2017
- 稿件说明：
移动端阅览
成怡, 白佳奇, 修春波. RGB-D视觉SLAM算法的改进及应用[J]. 光学精密工程, 2017,25(10s): 160-166

CHENG Yi, BAI Jia-qi, XIU Chun-bo. Improvement and application of RGB-D vision SLAM algorithm[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 160-166
成怡, 白佳奇, 修春波. RGB-D视觉SLAM算法的改进及应用[J]. 光学精密工程, 2017,25(10s): 160-166 DOI： 10.3788/OPE.20172513.0160.

CHENG Yi, BAI Jia-qi, XIU Chun-bo. Improvement and application of RGB-D vision SLAM algorithm[J]. Editorial Office of Optics and Precision Engineering, 2017,25(10s): 160-166 DOI： 10.3788/OPE.20172513.0160.

摘要

针对传统视觉SLAM算法位置误差积累及计算量大上的突出问题，提出了一种改进的RGB-D的视觉SLAM算法。在提出的算法中采用SURF算法提取特征，利用随机采样一致性和迭代最近点算法根据匹配结果估算并优化运动过程；在后端图优化的过程中，引入半随机的回环检测，通过改变关键帧的提取方法，克服当前帧最近检测与历史帧回环检测的缺点，提高匹配的准确性，同时减少了计算量。通过开源机器人操作系统的实验结果表明，提出的改进RGB-D的视觉SLAM算法处理一帧数据的速度为0.030 s左右，在满足了所得3D点云地图的准确性和完整性的基础上，加快了处理速度，节省了计算量，增强了算法的实时性。

Abstract

An improved visual SLAM algorithm of RGB-D was proposed aimed at prominent problem of accumulative position error and large calculated amount of traditional visual SLAM algorithm under the application background of wheeled mobile robot in the thesis. SURF algorithm was used to extract features

and random sample consensus (Ransac) and iterative closest point (ICP) algorithm were used to estimate and optimize movement process according to matching result in proposed algorithm; semi-random loop detection was introduced in the optimization process of back-end graph. Defects of recent detection of current frame and loop detection of historical frames were overcome by changing extraction method of key frames

which improved accuracy of matching and reduced calculated amount at the same time. Experimental result of open-source robot operating system (ROS) shows that speed of processing one frame data by proposed improved visual SLAM algorithm of RGB-D is 0.030 s approximately

which accelerates processing speed

saves calculated amount and strengthens real-time performance of the algorithm on the basis of satisfying accuracy and completeness of obtained 3D point cloud map.

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Keywords

references

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