VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

Chen, Danpeng; Wang, Shuai; Xie, Weijian; Zhai, Shangjin; Wang, Nan; Bao, Hujun

doi:10.1109/icra46639.2022.9812354

Cited by 19 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to the method outlined in [ 4 ], which utilizes 3D feature points for plane fitting, here, only the 2D feature points from previous frames and their matching relationships are used to prevent the influence of depth errors of road points. There is a similar method [ 48 ] that uses 2D feature points to construct homography between two keyframes. However, they use this constraint to optimize poses, whereas homography is employed to optimize planes in the proposed method.…”

Section: Proposed Methodsmentioning

confidence: 99%

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

Zhu,

An,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six Degrees of Freedom (DoF) motions in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

Zhu,

An,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…It uses planes for tracking and global graph optimization. Chen et al 14 presented a tightly coupled SLAM system that integrates RGB, depth, IMU, and structured plane information. While most plane features are typically extracted using RGB-D cameras, our work extracts plane information by utilizing the lines and corner points from scenes simultaneously with marker feature extraction.…”

Section: Related Workmentioning

confidence: 99%

A visual odometry with planar markers and feature point cues

Zhang,

Ning,

Liu

et al. 2024

International Workshop on Advanced Imaging Technology (IWAIT) 2024

View full text Add to dashboard Cite

“…Reyes-Aviles et al [5] proposed a registration method that leverages geometric constraints extracted from parametric primitives within a 3D parametric model, providing closed-form solutions for registration involving three types of correspondences: lines to lines, lines to planes, and planes to planes. Furthermore, line and planar features have gained popularity in the field of SLAM [41]- [44], as evidenced by various works.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sketch-Based Framework Utilizing Contour Cues for Efficient Point Cloud Registration

Ma,

Wei

2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Point cloud registration is a crucial part of 3D computer vision. Existing point cloud registration methods primarily concentrate on utilizing features such as points, lines, and planes, disregarding the valuable contour cues inherent in the scene. In this article, we propose a novel sketch-based framework for point cloud registration that incorporates contour cues to enhance the point cloud registration task. To fully exploit the abundant information provided by contour cues in the scene, the point cloud is first abstracted into a sketch consisting of contour cues obtained through the utilization of planar features, which greatly preserves the inherent contour information. Subsequently, a local contour geometric descriptor is introduced to encode the contour cues in the sketch. Finally, a voting-based Contour Point Pair Feature (CPPF) framework is employed to fuse planar features, local contour geometric features and point pair geometric features, enabling precise estimation of the pose transformation between pairwise point clouds. Extensive experiments conducted on two large-scale outdoor point cloud datasets and two indoor point cloud datasets validate the effectiveness of the proposed sketchbased method. Our proposed method successfully suppresses rotation and translation errors, ultimately achieving state-of-theart performance.

show abstract

VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM

Cited by 19 publications

References 28 publications

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

A visual odometry with planar markers and feature point cues

A Novel Sketch-Based Framework Utilizing Contour Cues for Efficient Point Cloud Registration

Contact Info

Product

Resources

About