Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

Prados, Carlos; Lorente, Sergio Villanueva; Castro, Mario Di

doi:10.3390/s21165340

Cited by 8 publications

(5 citation statements)

References 46 publications

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“…With this profile at hand, the State Manager Node issues directives to the subordinate level. Internally, the State Manager calculates the necessary body positioning to adhere to the planned trajectory and estimates the robot’s state (which can be implemented as indicated in [ 143 ]), the COM of the entire robotic organism, and the next position where a module should step. One of the main duties of the State Manager is to take into account the static and gripping stability of the commanded positions of the robot organism based on the modules and body positions, direction of gravity, and reaction forces computed in the suction cups.…”

Section: Robot Control Architecturesmentioning

confidence: 99%

A Review and Evaluation of Control Architectures for Modular Legged and Climbing Robots

Prados,

Hernando,

Gambao

et al. 2024

Biomimetics

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Robotic control is a fundamental part of autonomous robots. Modular legged and climbing robots are complex machines made up of a variety of subsystems, ranging from a single robot with simple legs to a complex system composed of multiple legs (or modules) with computing power and sensitivity. Their complexity, which is increased by the fact of needing elements for climbing, makes a correct structure crucial to achieve a complete, robust, and versatile system during its operation. Control architectures for legged robots are distinguished from other software architectures because of the special needs of these systems. In this paper, we present an original classification of modular legged and climbing robots, a comprehensive review of the most important control architectures in robotics, focusing on the control of modular legged and climbing robots, and a comparison of their features. The control architecture comparison aims to provide the analytical tools necessary to make informed decisions tailored to the specific needs of your robotic applications. This article includes a review and classification of modular legged and climbing robots, breaking down each category separately.

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Section: Robot Control Architecturesmentioning

confidence: 99%

A Review and Evaluation of Control Architectures for Modular Legged and Climbing Robots

Prados,

Hernando,

Gambao

et al. 2024

Biomimetics

Self Cite

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“…Body position estimation is implemented from [63]. It takes the robot and IMU odometry as input and fuses them with an Extended Kalman Filter (EKF).…”

Section: State Estimatormentioning

confidence: 99%

Torque-Based Control of a Bio-Inspired Modular Climbing Robot

et al. 2023

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This article presents a generalizable, low computational cost, simple, and fast gravity compensation method for legged robots with a variable number of legs. It is based on the static problem, which is a reduction in the dynamic model of the robot that takes advantage of the low velocity of climbing robots. To solve it, we propose a method that computes the torque to be applied by each actuator to compensate for the gravitational forces without using the Jacobian matrix for the forces exerted by the end-effector and without using analytical methods for the gravitational components of the model. We compare our method with the most popular method and conclude that ours is twice as fast. Using the proposed gravity compensator, we present a torque-based PD controller for the position of the leg modules, and a body velocity control without dynamic compensation. In addition, we validate the method with both hardware and a simulated version of the ROMERIN robot, a modular legged and climbing robot. Furthermore, we compare our controller with the usual kinematic inverse controllers, demonstrating that the mean angular and linear error is significantly reduced, as well as the power requirements of the actuators.

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“…In pose graph SLAM, problems are often formulated as a maximum likelihood estimate of the time discretized robot trajectory given odometric and loop closure measurements [9,10] through recognizing the previously visited place. Loop closure technique can mitigate the cumulative error and appearance-based loop-closure detection [4] has been widely used.…”

Section: Related Workmentioning

confidence: 99%

“…The second step of error rejection algorithm is presented by Algorithm 4. (8) if loop and left loop can be connected as shown in Figure 4: (9) Calculating whether the two loops satisfy mutual consistency through formula (10)…”

Section: The Second Step Of Error Rejection: Reject False Positive Loop-closure Detection Among Cooperative Robotsmentioning

confidence: 99%

Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems

Zhang

Wang

et al. 2021

Electronics

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Loop-closure detection is an essential means to reduce accumulated errors of simultaneous localization and mapping (SLAM) systems. However, even false positive loop closures could seriously interfere and even corrupt the back-end optimization process. For a collaborative SLAM system that generally uses both intra-robot and inter-robot loop closures to optimize the pose graph, it is a tough job to reject those false positive loop closures without a reliable a priori knowledge of the relative pose transformation between robots. Aiming at this solving problem, this paper proposes a two-stage false positive loop-closure rejection method based on three types of consistency checks. Firstly, a multi-robot pose-graph optimization model is given which transforms the multi-robot pose optimization problem into a maximum likelihood estimation model. Then, the principle of the false positive loop-closure rejection method based on χ2 test is proposed, in which clustering is used to reject those intra-robot false loop-closures in the first step, and a largest mutually consistent loop-based χ2 test is constructed to reject inter-robot false loop closures in the second step. Finally, an open dataset and synthetic data are used to evaluate the performance of the algorithms. The experimental results demonstrate that our method improves the accuracy and robustness of the back-end pose-graph optimization with a strong ability to reject false positive loop closures, and it is not sensitive to the initial pose at the same time. In the Computer Science and Artificial Intelligence Lab (CSAIL) dataset, the absolute position error is reduced by 55.37% compared to the dynamic scaling covariance method, and the absolute rotation error is reduced by 77.27%; in the city10,000 synthetic dataset, the absolute position error is reduced by 89.37% compared to the pairwise consistency maximization (PCM) and the absolute rotation error is reduced by 97.9%.

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Graph SLAM Built over Point Clouds Matching for Robot Localization in Tunnels

Cited by 8 publications

References 46 publications

A Review and Evaluation of Control Architectures for Modular Legged and Climbing Robots

A Review and Evaluation of Control Architectures for Modular Legged and Climbing Robots

Torque-Based Control of a Bio-Inspired Modular Climbing Robot

Using a Two-Stage Method to Reject False Loop Closures and Improve the Accuracy of Collaborative SLAM Systems

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