On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems

Chou, Chieh; Wang, Di; Song, Dezhen; Davis, Timothy A.

doi:10.1109/iros40897.2019.8967731

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…This technique involves a graph-based representation of the SLAM issue, where vertices represent robot poses and map characteristics and edges represent constraints or measurements between the poses. It is commonly used as a correction tool in graph-based SLAM types ( Zhang et al, 2017 ; Chou et al, 2019 ; Meng et al, 2022 ).…”

Section: Visual Slam Paradigmmentioning

confidence: 99%

A review of visual SLAM for robotics: evolution, properties, and future applications

Al-Tawil,

Hempel,

Abdelrahman

et al. 2024

Front. Robot. AI

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Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.

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Section: Visual Slam Paradigmmentioning

confidence: 99%

A review of visual SLAM for robotics: evolution, properties, and future applications

Al-Tawil,

Hempel,

Abdelrahman

et al. 2024

Front. Robot. AI

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“…The new particle point set {𝑥 𝑡 (𝑖) } uses multivariate normal distribution formula for probability calculation from 𝑥 𝑡 (𝑖) ~N(𝜇 𝑡 (𝑖) , 𝛴 𝑡 (𝑖) ) , and the proposal distribution π in its weight is improved to 𝑃(𝑥 𝑡 (𝑖) |𝑚 𝑡−1 (𝑖) , 𝑥 𝑡−1 (𝑖) , 𝑧 𝑡 , 𝑢 𝑡−1 ), so the weight calculation method corresponding to the new particle is Equation (9).…”

Section: Figure 3 Effect Of Observation Reliability On Observation Di...mentioning

confidence: 99%

“…At present, the graph optimization method is one of the academic hot topics [9], which uses the vertex as the optimization variable, and the observation equation as the constraint edge to construct the nonlinear least squares problem. Then, the error is iteratively minimized by gradient descending, Gauss-Newton, Levenberg-Marquart or other methods to calculate the global optimal poses.…”

Section: Introductionmentioning

confidence: 99%

Lidar SLAM Based on Particle Filter and Graph Optimization for Substation Inspection

Zeng

et al. 2022

IEEE Access

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This work was supported by state grid corporation under science and technology project "Research and application of visual and auditory active perception and collaborative cognition technology for smart grid operation and maintenance scenarios" (Grant: 5600-202046347A-0-0-00) ABSTRACT Simultaneous Localization and Mapping (SLAM) is the core technology of intelligent substation inspection robot. Because of lightweight computation, Rao-Blackwellized Particle Filter (RBPF) is widely used in two-dimensional SLAM. However, it suffers from poor positioning accuracy, low robustness and rapid cumulative errors despite recent improvement. This paper presents a lidar SLAM system based on RBPF and graph optimization that can adapt to unstructured operating environment of substation. Firstly, the diversity of particles is increased by rebuilding the resample algorithm to improve the robustness of the system, and high-quality poses are estimated in submaps. Secondly, the multi-submap system is established to construct odometry constraints (one pose corresponds to two submaps). Furthermore, loop detector is an important part of optimization algorithm, and the branch-bound method is used to reduce computation burden and accelerate the loop detection. Finally, global poses of robot are optimized by the whole odometry and loop constraints in real time. Experiment results show that the proposed method is more accurate than other methods, and can maintain and produce high-precision positioning and mapping in complex substation operation and maintenance environment. It provides a new idea for intelligent substation inspection and positioning method.

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“…computes the distance between UWB node k on robot i and UWB node j on robot k given a pose x. The minimization in Equation 1 is considered as a nonlinear optimization problem, which is solved by general graph optimization (g2o) [23] in this paper. In particular, the pose to be estimated is denoted as the node in the graph and the constraints are represented by the UWB ranging measurements.…”

Section: B Relative Pose Estimation Based On Uwb Rangingsmentioning

confidence: 99%

Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements

Cao¹,

Liu²,

Yuen³

et al. 2021

Preprint

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Relative localization between autonomous robots without infrastructure is crucial to achieve their navigation, path planning, and formation in many applications, such as emergency response, where acquiring a prior knowledge of the environment is not possible. The traditional Ultra-WideBand (UWB)-based approach provides a good estimation of the distance between the robots, but obtaining the relative pose (including the displacement and orientation) remains challenging. We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes. We determine the pose between two robots by minimizing the residual error of the ranging measurements from all UWB nodes. To improve the localization accuracy, we propose to utilize the odometry constraints through a sliding window-based optimization. The optimized pose is then fused with the odometry in a particle filtering for pose tracking among a group of mobile robots. We have conducted extensive experiments to validate the effectiveness of the proposed approach.

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On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems

Cited by 6 publications

References 31 publications

A review of visual SLAM for robotics: evolution, properties, and future applications

A review of visual SLAM for robotics: evolution, properties, and future applications

Lidar SLAM Based on Particle Filter and Graph Optimization for Substation Inspection

Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements

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