Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (4): 1437-1446.doi: 10.13229/j.cnki.jdxbgxb20200380

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DG-SLAM algorithm for dynamic scene compound deep learning and parallel computing

Feng-chong LAN1,2(),Ji-wen LI1,2,Ji-qing CHEN1,2()   

  1. 1.School of Mechanical & Automotive Engineering，South China University of Technology，Guangzhou 510640，China
    2.Guangdong Key Laboratory of Automotive Engineering，South China University of Technology，Guangzhou 510640，China
  • Received:2020-05-30 Online:2021-07-01 Published:2021-07-14
  • Contact: Ji-qing CHEN E-mail:fclan@scut.edu.cn;chjq@scut.edu.cn

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Abstract:

In view of the disadvantages of the existing simultaneous localization and mapping (SLAM) algorithm, which has low real-time performance and the positioning accuracy is greatly reduced in dynamic environment, a DG-SLAM algorithm based on deep learning and GPU parallel computing was proposed. The deep learning-based object detection algorithm was introduced to detect dynamic objects in the driving environment, and the feature points of dynamic objects were removed before the matching of image frames, so as to eliminate the impact of mismatching dynamic objects on the positioning accuracy of SLAM system. In the tracking of local maps of ORB-SLAM2, the discriminant method of 3D interior points was used to distinguish the inner points and outer points, and the GPU parallel computing model was established to efficiently search the local map points. The Saturated kernel function was used to minimize the reprojection error of the two norm terms to ensure the parallel calculation of the reprojection error when the local map was optimizing. The algorithm was verified on the KITII dataset, DG-SLAM has high tracking accuracy, and the average calculation efficiency was more than 3.4 times faster than that of ORB-SLAM2 system under the same conditions, more than 85 frames per second, which could realize efficient and high precision SLAM in dynamic scene.

Key words: vehicle engineering, simultaneous localization and mapping, deep learning, object detection, parallel computing

CLC Number: 

  • TP391.4

Fig.1

Overall system architecture of DG-SLAM"

Fig.2

ORB-SLAM2 positioning process"

Fig.3

Parallel computing process for localization of DG-SLAM"

Fig. 4

Variation trend of saturated kernel function under different residual thresholds"

Fig.5

SSD network structure"

Fig.6

Data association comparison diagram before and after removing dynamic feature points"

Fig.7

KITTI dataset road types"

Fig.8

Comparison of position estimation between DG-SLAM and ORB-SLAM2 on KITTI 01 dataset"

Fig.9

Comparison of attitude angle estimation between DG-SLAM and ORB-SLAM2 on KITTI 01 dataset"

Fig.10

Trajectory error of ORB-SLAM2 tested on KITTI 01 dataset"

Fig.11

Trajectory error of DG-SLAM tested on KITTI 01 dataset"

Fig.12

Absolute pose errors of DG-SLAM and ORB-SLAM2"

Table 1

Test accuracy on KITTI dataset"

序列编号场景类型轨迹长度/mRMSE
ORB?SLAM2DG?SLAM
平均值--20.73717.692
00城市支路714.269.6169.560
01高速公路2453.2011.7461.666
02城市主干路5067.2347.15028.354
03城市主干路560.880.9200.918
04城市快速路393.640.7030.423
05城市支路2205.5744.32444.532
06城市支路1232.8729.34927.258
07城市支路694.6917.87316.828
08城市支路3222.7955.18452.689
09城市主干路1705.0558.73349.568
10城市支路919.516.3846.356

Fig.13

Absolute pose errors of DG-SLAM and ORB-SLAM2 on KITTI 04"

Fig.14

Absolute pose errors of DG-SLAM and ORB-SLAM2 on KITTI 02"

Fig.15

Absolute pose errors of DG-SLAM and ORB-SLAM2 on KITTI 03"

Fig.16

Comparison of time consuming on KITTI 01 dataset between DG-SLAM and ORB-SLAM2"

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