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浙江大学学报(工学版)
浙江大学学报(工学版)  2022, Vol. 56 Issue (6): 1062-1070    DOI: 10.3785/j.issn.1008-973X.2022.06.002
智能机器人     
基于重投影深度差累积图与静态概率的动态RGB-D SLAM算法
林凯(),梁新武*(),蔡纪源
上海交通大学 航空航天学院，上海 200240
Dynamic RGB-D SLAM algorithm based on reprojection depth difference cumulative map and static probability
Kai LIN(),Xin-wu LIANG*(),Ji-yuan CAI
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
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摘要：

为了提高同时定位与建图（SLAM）系统在动态场景下的定位精度和鲁棒性，提出新的RGB-D SLAM算法. 建立基于重投影深度差值的累积模型，分割图像的动静态区域；为了避免动态区域过分割，先剔除与匹配地图点欧氏距离过大的动态区域特征点，再根据t分布估计其余特征点的静态概率；将静态区域特征点和动态区域的疑似静态点以不同权重加入位姿优化，得到提纯后的位姿. 在公开数据集上的实验结果表明，所提算法在动态场景下较改进前的RGB-D ORB-SLAM2算法的定位精度提升96.1%，较其他动态SLAM算法提升31.2%，有效提高了视觉SLAM系统在动态环境下的定位精度和鲁棒性.
关键词： 动态环境;  视觉SLAM;  RGB-D相机;  重投影深度差累积图;  静态概率    
Abstract:

To improve the localization accuracy and the robustness of simultaneous localization and mapping (SLAM) in dynamic scenes, a new RGB-D SLAM algorithm was proposed. Firstly, a cumulative model based on the reprojection depth difference was built to segment the dynamic and static region in the image. Secondly, to avoid over-segmentation, the feature points in the dynamic region whose Euclidean distances were too large from the matching map point were eliminated. The static probabilities of other feature points were estimated according to the t-distribution. Finally, the feature points in the static region and the suspected static points in the dynamic region were added into the pose optimization with different weights to refine the pose. Experiments with public datasets showed that in dynamic scenes, the localization accuracy of the proposed method was improved by 96.1% compared with RGB-D ORB-SLAM2 and 31.2% compared with other dynamic SLAM algorithms. The localization accuracy and robustness of the visual SLAM system in dynamic scenes were effectively improved.
Key words: dynamic environment    visual SLAM    RGB-D camera    reprojection depth difference cumulative map    static probability
收稿日期: 2021-04-28 出版日期: 2022-06-30
CLC:  TP 242  
基金资助: 国家自然科学基金资助项目（62173230）
通讯作者: 梁新武     E-mail: link013@sjtu.edu.cn;xinwuliang@sjtu.edu.cn
作者简介: 林凯（1995—），男，硕士生，从事计算机视觉研究. orcid.org/0000-0003-2401-4354. E-mail： link013@sjtu.edu.cn
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林凯
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引用本文:

林凯,梁新武,蔡纪源. 基于重投影深度差累积图与静态概率的动态RGB-D SLAM算法[J]. 浙江大学学报(工学版), 2022, 56(6): 1062-1070.

Kai LIN,Xin-wu LIANG,Ji-yuan CAI. Dynamic RGB-D SLAM algorithm based on reprojection depth difference cumulative map and static probability. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1062-1070.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.06.002    或    https://www.zjujournals.com/eng/CN/Y2022/V56/I6/1062

图 1  基于重投影深度差累积图与静态概率的动态RGB-D SLAM算法框架图
图 2  匹配点对的欧氏距离
图 3  静态概率估计算法流程图
图 4  动态区域检测结果
图 5  动态区域的静态概率估计结果
序列 $R_{\rm{t}}$/(m·s ?1) $R_{\rm{r}}$/((°)·s ?1)
eDVO [ 14] ORB-VO eORB-VO 本研究 eDVO [ 14] ORB-VO eORB-VO 本研究
static_board 0.292 0.034 0.022 0.024 4.839 0.018 0.009 0.010
static_construct 0.153 0.033 0.003 0.003 3.821 0.017 0.001 0.001
dynamic_board 0.111 X 0.027 0.028 1.939 X 0.031 0.031
dynamic_man1 0.157 0.039 0.025 0.023 4.108 0.053 0.053 0.053
fr3/sitting_xyz 0.073 0.008 0.010 0.009 1.860 0.008 0.008 0.008
fr3/walking_static 0.217 0.016 0.013 0.012 0.197 0.008 0.006 0.006
fr3/walking_xyz 0.259 0.028 0.020 0.015 4.069 0.016 0.012 0.010
表 1  LARR和TUM RGB-D数据集上不同算法的相对位姿误差（均方根误差）对比
图 6  本研究算法与ORB-VO相对位姿误差曲线对比图
m
序列 ATE
ORB-SLAM2 eORB-SLAM2 本研究
static_board 1.442 6 0.056 4 0.077 4
static_construct 1.114 7 0.004 6 0.004 7
dynamic_board 1.151 0 0.021 3 0.021 6
dynamic_man1 0.297 7 0.035 1 0.034 2
fr3/sitting_xyz 0.009 2 0.017 2 0.010 5
fr3/walking_static 0.393 1 0.021 0 0.012 7
fr3/walking_xyz 0.676 8 0.426 6 0.054 4
fr3/walking_halfsphere 0.648 3 0.119 4 0.047 4
fr3/walking_rpy 0.787 1 0.050 9 0.033 1
平均 0.724 5 0.111 7 0.028 6
表 2  LARR和TUM RGB-D数据集上本研究算法与ORB-SLAM2的绝对轨迹误差（均方根）对比
图 7  本研究算法与ORB-SLAM2估计轨迹对比图
TUM_fr3序列 ATE
半直接法 [ 3] Detect-SLAM [ 6] DS-SLAM [ 8] 改进几何与运动约束 [ 11] DSLAM [ 12] Static-weight [ 13] 本研究
sitting_static — — 0.006 4 0.044 6 0.009 6 — 0.006 9
sitting_xyz 0.011 3 0.020 1 — 0.018 6 0.009 1 0.039 7 0.010 5
sitting_halfsphere 0.062 0 0.023 1 — 0.027 1 0.023 5 0.043 2 0.017 0
sitting_rpy — — — 0.038 5 0.022 5 — 0.026 1
walking_static 0.008 0 — 0.008 1 0.013 1 0.010 8 0.026 1 0.012 7
walking_xyz 0.037 1 0.024 1 0.024 7 0.035 4 0.087 4 0.060 1 0.054 4
walking_halfsphere 0.040 9 0.051 4 0.030 3 0.028 5 0.035 4 0.048 9 0.047 4
walking_rpy X 0.295 9 0.444 2 0.096 6 0.160 8 0.179 1 0.033 1
平均 X 0.082 9 0.102 7 0.037 8 0.044 9 0.066 2 0.026 0
表 3  TUM RGB-D数据集上不同算法的绝对轨迹误差（均方根误差）对比
ms
算法 操作 t ave
sitting_static walking_static
ORB-SLAM2 跟踪 26.98 39.76
本研究 跟踪 379.66 412.07
动静态分割 350.97 372.18
静态概率估计 1.00 1.03
表 4  本研究算法与ORB-SLAM2的时间效率对比
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