Edge-Based Robust RGB-D Visual Odometry Using 2-D Edge Divergence Minimization

Kim, Changhyeon; Kim, Pyojin; S, Lee; Kim, H. Jin

doi:10.1109/iros.2018.8593594

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…To overcome this limitation, [46] uses both points and lines for RGB-D SLAM. In [47]- [51] and [52], direct edge alignment is proposed that minimizes the sum of squared distances between the reprojected and the nearest edge point using the distance transform of the edge-map, with other errors like photometric error or ICP-based point-to-plane distance minimized together [1].…”

Section: Edge-based and Plane-base Methodsmentioning

confidence: 99%

Survey and Evaluation of RGB-D SLAM

2021

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The traditional visual SLAM systems take the monocular or stereo camera as input sensor, with complex map initialization and map point triangulation steps needed for 3D map reconstruction, which are easy to fail, computationally complex and can cause noisy measurements. The emergence of RGB-D camera which provides RGB image together with depth information breaks this situation. While a number of RGB-D SLAM systems have been proposed in recent years, the current classification research on RGB-D SLAM is very lacking, and their advantages and shortcomings remain unclear regarding different applications and perturbations, such as illumination transformation, noise and rolling shutter effect of sensors. In this paper, we mainly introduced the basic concept and structure of the RGB-D SLAM system, and then introduced the differences between the various RGB-D SLAM systems in the three aspects of tracking, mapping, and loop detection, and we make a classification study on different RGB-D SLAM algorithms according to the three aspect. Furthermore, we discuss some advanced topics and open problems of RGB-D SLAM, hoping that it will help for future exploring. In the end, we conducted a large number of evaluation experiments on multiple RGB-D SLAM systems, and analyzed their advantages and disadvantages, as well as performance differences in different application scenarios, and provided references for researchers and developers.

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Section: Edge-based and Plane-base Methodsmentioning

confidence: 99%

Survey and Evaluation of RGB-D SLAM

2021

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“…Other works propose to jointly minimize this edge distance and other errors, e.g., a photometric error [114] and an ICP-based point-to-plane distance [93]. Later works such as [124] and [56] take the image gradient direction also into account for the direct edge alignment. As in [55], these last two works estimate the camera pose using the iteratively reweighted least-squares (IRLS) method with the t-distribution as a robust weight function.…”

Section: Edge-based Methodsmentioning

confidence: 99%

RGB-D Odometry and SLAM

Civera

Lee

2019

Advances in Computer Vision and Pattern Recognition

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The emergence of modern RGB-D sensors had a significant impact in many application fields, including robotics, augmented reality (AR) and 3D scanning. They are low-cost, low-power and low-size alternatives to traditional range sensors such as LiDAR. Moreover, unlike RGB cameras, RGB-D sensors provide the additional depth information that removes the need of frame-by-frame triangulation for 3D scene reconstruction. These merits have made them very popular in mobile robotics and AR, where it is of great interest to estimate egomotion and 3D scene structure. Such spatial understanding can enable robots to navigate autonomously without collisions and allow users to insert virtual entities consistent with the image stream. In this chapter, we review common formulations of odometry and Simultaneous Localization and Mapping (known by its acronym SLAM) using RGB-D stream input. The two topics are closely related, as the former aims to track the incremental camera motion with respect to a local map of the scene, and the latter to jointly estimate the camera trajectory and the global map with consistency. In both cases, the standard approaches minimize a cost function using nonlinear optimization techniques. This chapter consists of three main parts: In the first part, we introduce the basic concept of odometry and SLAM and motivate the use of RGB-D sensors. We also give mathematical preliminaries relevant to most odometry and SLAM algorithms. In the second part, we detail the three main components of SLAM systems: camera pose tracking, scene mapping and loop closing. For each component, we describe different approaches proposed in the literature. In the final part, we provide a brief discussion on advanced research topics with the references to the state-of-the-art.

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“…Even though this method enhances the robustness to scenes with texture loss, it has poor stability in complex environments (such as illumination). Te other as the main feature, several methods have been proposed in [99][100][101], but these methods all have the defects of edge feature extraction failure and edge feature redundancy.…”

Section: Te Multifeature V-slammentioning

confidence: 99%

A Review of Common Techniques for Visual Simultaneous Localization and Mapping

Dai

Wang

2023

Journal of Robotics

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Mobile robots are widely used in medicine, agriculture, home furnishing, and industry. Simultaneous localization and mapping (SLAM) is the working basis of mobile robots, so it is extremely necessary and meaningful for making researches on SLAM technology. SLAM technology involves robot mechanism kinematics, logic, mathematics, perceptual detection, and other fields. However, it faces the problem of classifying the technical content, which leads to diverse technical frameworks of SLAM. Among all sorts of SLAM, visual SLAM (V-SLAM) has become the key academic research due to its advantages of low price, easy installation, and simple algorithm model. Firstly, we illustrate the superiority of V-SLAM by comparing it with other localization techniques. Secondly, we sort out some open-source V-SLAM algorithms and compare their real-time performance, robustness, and innovation. Then, we analyze the frameworks, mathematical models, and related basic theoretical knowledge of V-SLAM. Meanwhile, we review the related works from four aspects: visual odometry, back-end optimization, loop closure detection, and mapping. Finally, we prospect the future development trend and make a foundation for researchers to expand works in the future. All in all, this paper classifies each module of V-SLAM in detail and provides better readability to readers. This is undoubtedly the most comprehensive review of V-SLAM recently.

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Edge-Based Robust RGB-D Visual Odometry Using 2-D Edge Divergence Minimization

Cited by 16 publications

References 20 publications

Survey and Evaluation of RGB-D SLAM

Survey and Evaluation of RGB-D SLAM

RGB-D Odometry and SLAM

A Review of Common Techniques for Visual Simultaneous Localization and Mapping

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