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Dynamic SLAM visual odometry Based on Instance Segmentation:A Comprehensive Review

Computers, Materials & Continua 2024年第1期78卷 167-196页

作者： Jiansheng Peng Qing Yang Dunhua Chen Chengjun Yang Yong Xu Yong Qin College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous ***,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map *** the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic *** to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s *** article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and *** comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic ***,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM *** recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance ***,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

关键词： Dynamic SLAM instance segmentation visual odometry

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Human visual Attention Mechanism-Inspired Point-and-Line Stereo visual odometry for Environments with Uneven Distributed Features

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Chinese Journal of Mechanical Engineering 2023年第3期36卷 191-204页

作者： Chang Wang Jianhua Zhang Yan Zhao Youjie Zhou Jincheng Jiang School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China School of Mechanical Engineering Shandong UniversityJinan 250100China School of Mechanical Engineering Hebei University of TechnologyTianjin 300401China

visual odometry is critical in visual simultaneous localization and mapping for robot ***,the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense features occupy excessive ***,a new human visual attention mechanism for point-and-line stereo visual odometry,which is called point-line-weight-mechanism visual odometry(PLWM-VO),is proposed to describe scene features in a global and balanced manner.A weight-adaptive model based on region partition and region growth is generated for the human visual attention mechanism,where sufficient attention is assigned to position-distinctive objects(sparse features in the environment).Furthermore,the sum of absolute differences algorithm is used to improve the accuracy of initialization for line *** with the state-of-the-art method(ORB-VO),PLWM-VO show a 36.79%reduction in the absolute trajectory error on the Kitti and Euroc *** the time consumption of PLWM-VO is higher than that of ORB-VO,online test results indicate that PLWM-VO satisfies the real-time *** proposed algorithm not only significantly promotes the environmental adaptability of visual odometry,but also quantitatively demonstrates the superiority of the human visual attention mechanism.

关键词： visual odometry Human visual attention mechanism Environmental adaptability Uneven distributed features

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A robust RGB-D visual odometry with moving object detection in dynamic indoor scenes

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IET Cyber-Systems and Robotics 2023年第1期5卷 79-88页

作者： Xianglong Zhang Haiyang Yu Yan Zhuang College of Mechanical and Electronic Engineering Dalian Minzu UniversityDalianChina School of Control Science and Engineering Dalian University of TechnologyDalianChina

Simultaneous localisation and mapping(SLAM)are the basis for many robotic *** the front end of SLAM,visual odometry is mainly used to estimate camera *** dynamic scenes,classical methods are deteriorated by dynamic objects and cannot achieve satisfactory *** order to improve the robustness of visual odometry in dynamic scenes,this paper proposed a dynamic region detection method based on RGBD ***,all feature points on the RGB image are classified as dynamic and static using a triangle constraint and the epipolar geometric constraint ***,the depth image is clustered using the K-Means *** classified feature points are mapped to the clustered depth image,and a dynamic or static label is assigned to each cluster according to the number of dynamic feature ***,a dynamic region mask for the RGB image is generated based on the dynamic clusters in the depth image,and the feature points covered by the mask are all *** remaining static feature points are applied to estimate the camera ***,some experimental results are provided to demonstrate the feasibility and performance.

关键词： dynamic indoor scenes moving object detection RGB-D SLAM visual odometry

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Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online

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Frontiers of Information Technology & Electronic Engineering 2015年第2期16卷 152-165页

作者： Wei LU Instttute of lnformat:on and Communtcatlon Engineering Zhejtang Umvers:ty Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry （VO） and simultaneous localization and mapping （SLAM）, suffer from two shortcomings： a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks：（1） detect- ing road junctions and building a landmark library online; （2） correcting the robot＇s position when the landmarks are revisited with any orientation; （3） working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adap- tive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment （TLBBA） stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.

关键词： visual odometry Panoramic landmark Landmark matching Compressed sensing Adaptive compressive feature

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Fast and accurate visual odometry from a monocular camera

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Frontiers of Computer Science 2019年第6期13卷 1326-1336页

作者： Xin YANG Tangli XUE Hongcheng LUO Jiabin GUO School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhan 430074China

This paper aims at a semi-dense visual odometry system that is accurate,robust,and able to run realtime on mobile devices,such as smartphones,AR glasses and small *** key contributions of our system include:1)the modified pyramidal Lucas-Kanade algorithm which incorporates spatial and depth constraints for fast and accurate camera pose estimation;2)adaptive image resizing based on inertial sensors for greatly accelerating tracking speed with little accuracy degradation;and 3)an ultrafast binary feature description based directly on intensities of a resized and smoothed image patch around each pixel that is sufficiently effective for relocalization.A quantitative evaluation on public datasets demonstrates that our system achieves better tracking accuracy and up to about 2X faster tracking speed comparing to the state-of-the-art monocular SLAM system:*** the relocalization task,our system is 2.0X∼4.6X faster than DBoW2 and achieves a similar accuracy.

关键词： visual odometry mobile devices direct tracking relocalization inertial sensing binary feature

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Overfitting Reduction of Pose Estimation for Deep Learning visual odometry

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China Communications 2020年第6期17卷 196-210页

作者： Xiaohan Yang Xiaojuan Li Yong Guan Jiadong Song Rui Wang Information Engineering College Capital Normal UniversityBeijing 100048China Beijing Engineering Research Center of High Reliable Embedded System Beijing Advanced Innovation Center for Imaging Theory and Technology Machinery Industry Information Center Beijing 100823China Beijing Key Laboratory of Light Industrial Robots and Safety Verification

Error or drift is frequently produced in pose estimation based on geometric"feature detection and tracking"monocular visual odometry(VO)when the speed of camera movement exceeds 1.5 m/***,in most VO methods based on deep learning,weight factors are in the form of fixed values,which are easy to lead to overfitting.A new measurement system,for monocular visual odometry,named Deep Learning visual odometry(DLVO),is proposed based on neural *** this system,Convolutional Neural Network(CNN)is used to extract feature and perform feature ***,Recurrent Neural Network(RNN)is used for sequence modeling to estimate camera’s 6-dof *** of fixed weight values of CNN,Bayesian distribution of weight factors are introduced in order to effectively solve the problem of network *** 18,726 frame images in KITTI dataset are used for training *** system can increase the generalization ability of network model in prediction *** with original Recurrent Convolutional Neural Network(RCNN),our method can reduce the loss of test model by 5.33%.And it’s an effective method in improving the robustness of translation and rotation information than traditional VO methods.

关键词： visual odometry neural network pose estimation bayesian distribution overfitting

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A Study on Planetary visual odometry Optimization： Time Constraints and Reliability

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Computer Technology and Application 2011年第5期2卷 378-388页

作者： Enrica Zereik Davide Ducco Fabio Frassinelli Giuseppe Casalino Department of Communication Computer and System Sciences Faculty of Engineering University of Genoa Genoa 16145 Italy

Robust and efficient vision systems are essential in such a way to support different kinds of autonomous robotic behaviors linked to the capability to interact with the surrounding environment, without relying on any a priori knowledge. Within space missions, above all those involving rovers that have to explore planetary surfaces, vision can play a key role in the improvement of autonomous navigation functionalities： besides obstacle avoidance and hazard detection along the traveling, vision can in fact provide accurate motion estimation in order to constantly monitor all paths executed by the rover. The present work basically regards the development of an effective visual odometry system, focusing as much as possible on issues such as continuous operating mode, system speed and reliability.

关键词： visual odometry stereo vision speeded up robust feature (SURF) planetary rover

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Improved visual odometry Based on SSD Algorithm in Dynamic Environment

Improved Visual Odometry Based on SSD Algorithm in Dynamic E...

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第三十九届中国控制会议

作者： Enbao Wang Yali Zhou Qizhi Zhang School of Automation Beijing Information Science ＆ Technology University

In recent years, Simultaneous Localization and Mapping（SLAM） have made great progress in the field of ***, the majority of SLAM algorithms are currently designed and processed in static environments. In the dynamic environment, due to the existence of moving objects, the SLAM system is easy to mismatching the pose estimation, which will affect its positioning accuracy and mapping accuracy. In this paper, the traditional Oriented FAST and Rotated Brief（ORB）-SLAM2 system is improved to deal with these problems. Firstly, the Single Shot MultiBox Detector（SSD）algorithm is used to detect the dynamic targets in the scene, and then the multi-layer optical flow is constructed to remove the dynamic feature points by optical flow tracking. Secondly, the static feature points in the scene are matched and the pose are calculated to solve the problems caused by a large number of matches, such as large system computation and low operating efficiency. Finally, testing on the TUM datasets shows that the absolute trajectory error and the relative pose error of the improved ORB-SLAM2 system are significantly reduced compared to the traditional ORB-SLAM2 system.

关键词： visual odometry Dynamic Environment ORB-SLAM2 SSD Multi-layer Optical Flow

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Ultra-wideband and visual odometry Based Relative Localization for Multi-UAV System

Ultra-wideband and Visual Odometry Based Relative Localizati...

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第三十九届中国控制会议

作者： Yikai Zhang Bailing Tian Hongming Chen School of Electrical and Information Engineering Tianjin University

This paper proposes a relative localization algorithm for Multi-UAV system, in which each agent is able to estimate the relative position of its neighbor based on Ultra-wideband measurement and visual odometry information exchanged. The relative localization algorithm consists of two parts. visual odometry pre-process part fuses the IMU data and radar altimeter data to correct visual odometry error and reach a higher update rate to ensure flight smoothly and a better state *** the UWB measurement and information exchanged, each agent constructs a graph to solve relative localization problem in sliding window. Finally, experimental results are presented to corroborate the effectiveness of our proposed relative localization algorithm.

关键词： Ultra-wideband relative localization visual odometry Multi-UAV system

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Deep Direct visual odometry Based on Unsupervised Ego-motion Estimation

Deep Direct Visual Odometry Based on Unsupervised Ego-motion...

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第32届中国过程控制会议(CPCC2021)

作者： Chaoqiang Zhao Yang Tang East China University of Science and Technology

Traditional monocular direct visual odometry(DVO) heavily relies on high-quality images and accurate initial pose estimation during tracking,which means that DVO may fail if the image quality is poor or the initial value is *** the outstanding performance of deep learning,previous works have shown that deep neural networks can effectively learn the 6-DOF pose between frames from large volumes of image sequences in an unsupervised ***,these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scaleinconsistency between each *** tackle this problem,we take several measures to improve the scale-consistency of our network(TrajNet),including improving the previous loss function and proposing a novel scale-to-trajectory ***,considering the lack of mapping thread in deep learning-based visual odometry(VO),a new architecture,called deep direct sparse odometry(DDSO),is proposed to overcome the limitations of DVO as well as the mapping of deep learning-based VO by embedding our TrajNet into *** experiments on the KITTI dataset show that the proposed network achieves an outstanding performance on full trajectory prediction when compared with previous unsupervised monocular methods,and the integration with our TrajNet makes the initialization and tracking of DVO more robust and accurate.

关键词： visual odometry direct methods pose estimation image processing unsupervised learning

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