A SLAM in Dynamic Environment Based on Instance Segmentation and Optical Flow

被引：0

作者：

Yue S. ^{[1
]}

Wang Z. ^{[1
]}

机构：

[1] School of Electromechanical Engineering, Beijing Institute of Technology, Beijing

来源：

Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 01期

关键词：

dynamic environment; instance segmentation; mapping; optical flow method; semantic simultaneous localization;

D O I：

10.12382/bgxb.2023.0568

中图分类号：

学科分类号：

摘要：

A visual semantic SLAM algorithm based on instance segmentation and optical flow is proposed to address the issue of excessive removal of features by traditional semantic SLAM algorithms in dynamic environments. The proposed algorithm utilizes a Mask R-CNN network to perform the instance-level segmentation of potential dynamic objects in an image, and also identifies and eliminates dynamic objects in the optical flow thread. The remaining static optical flow points and static feature points are then used to optimize the location estimation process, ensuring the optimal utilization of both semantic and optical flow information. The proposed algorithm is validated through testing on open datasets and an unmanned ground platform experiment. The experimental results indicate that the average error of the proposed algorithm is 75% and 8.5% lower than those of ORB-SLAM2 and Dyna-SLAM, respectively, on TUM dataset. © 2024 China Ordnance Industry Corporation. All rights reserved.

引用

页码：156 / 165

页数：9

共 27 条

[1]

CADENA C, CARLONE L, CARRILLO H, Et al., Past, present, and future of simultaneous localization and mapping: toward the robust-perception age, IEEE Transactions on Robotics, 32, 6, pp. 1309-1332, (2016)

[2]

TAKETOMI T, UCHIYAMA H, IKEDA S., Visual SLAM algorithms: a survey from 2010 to 2016[J], IPSJ Transactions on Computer Vision and Applications, 9, 1, (2017)

[3]

ZHANG F B, ZHANG B S, YANG Y S., SLAM algorithm based on monocular/IMU/odometer fusion, Acta Armamentarii, 43, 11, pp. 2810-2818, (2022)

[4]

MUR-ARTAL R, TARDOS D J., ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras [J], IEEE Transactions on Robotics, 33, 5, pp. 1255-1262, (2017)

[5]

LIU Q P, WANG Z J, WANG H., Navigation algorithm of light UAV based on stereo visual inertial navigation odometer, Acta Armamentarii, 41, pp. 241-248, (2020)

[6]

ENGEL J, KOLTUN V, CREMERS D., Direct sparse odometry [J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 3, pp. 611-625, (2018)

[7]

SUN Y X, LIU M, MENG M Q H., Improving RGB-D SLAM in dynamic environments: a motion removal approach, Robotics and Autonomous Systems, 89, pp. 110-122, (2016)

[8]

LI S L, LEE D., RGB-D SLAM in dynamic environments using static point weighting[J], IEEE Robotics and Automation Letters, 2, 4, pp. 2263-2270, (2017)

[9]

WANG Y B, HUANG S D., Towards dense moving object segmentation based robust dense RGB-D SLAM in dynamic scenarios, Proceedings of IEEE International Conference on Control, Automation, Robotics & Vision, pp. 1841-1846, (2014)

[10]

TAN W, LIU H M, DONG Z L, Et al., Robust monocular SLAM in dynamic environments, Proceedings of IEEE International Symposium on Mixed and Augmented Reality, pp. 209-218, (2013)

← 1 2 3 →