Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time

Handa, Asahi; Suzuki, Azumi; Date, Hisashi; Mitsudome, Ryohsuke; Tsubouchi, Takashi; Ohya, Akihisa

doi:10.20965/jrm.2019.p0180

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…FastSLAM is a combination algorithm, the central idea of which is to use recursive algorithm to estimate robot posture and calculate the complete posterior distribution of map signposts, so as to realize map creation and location. Literature [22] puts forward an optimized resampling algorithm to make the sub-plasma move towards the superior particles.…”

Section: Related Workmentioning

confidence: 99%

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Yating

2022

Wireless Communications and Mobile Computing

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In the industrial field, industrial robots have taken over the heavy lifting that used to be done by traditional handicraft assembly lines, greatly freeing up human resources and improving production efficiency and safety. As a result, the focus of this paper is on the SLAM-based robot localization and navigation algorithm (simultaneous localization and mapping). An attitude estimation algorithm based on KF (Kalman filtering) information fusion of vision SLAM and IMU (Inertial Measurement Unit) is proposed, and the ORB-SLAM algorithm is studied and perfected. The fusion of the two postures improves the accuracy and frequency of the robot’s attitude estimation during motion. In addition, PSO (Particle Swarm Optimization) technology is used to optimize the resampling process, and PSO optimizes the particle set to alleviate the problem of particle degradation and exhaustion caused by resampling in the FastSLAM algorithm. Finally, the algorithm is verified to meet the requirements of positioning and composition accuracy, as well as the feasibility and effectiveness of robot autonomous navigation, using the open simulation platform.

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Section: Related Workmentioning

confidence: 99%

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Yating

2022

Wireless Communications and Mobile Computing

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Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments

Sun¹,

Singh²,

Kanehiro³

2021

JRM

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Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.

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Self-Localization Using Trajectory Attractors in Outdoor Environments

Yamane,

Akutsu

2023

J. Robot. Mechatron.

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Self-localization in probabilistic robotics requires detailed, geographically consistent environmental maps, which increases the computational cost. In this study, we propose a simple self-localization method that does not require such maps. In the proposed method, the order structure, such as the mobile robot’s navigation route, is embedded as trajectory attractors in the state space of a nonmonotone neural network, and self-position estimation is performed by processing based on the autonomous dynamics of the network. From experiments, we demonstrated the basic performance of the proposed method, including robust self-localization in complex outdoor environments. Furthermore, self-localization is possible on multiple courses with overlapping paths by suitably varying the network dynamics based on environmental information. While issues remain, this study points to the great potential of neurodynamics-based robotic self-localization.

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Navigation Based on Metric Route Information in Places Where the Mobile Robot Visits for the First Time

Cited by 4 publications

References 16 publications

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments

Self-Localization Using Trajectory Attractors in Outdoor Environments

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