Map point selection for visual SLAM

Müller, Christopher; Daalen, Corné E. van

doi:10.1016/j.robot.2023.104485

Cited by 1 publication

(1 citation statement)

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“…Simultaneous localization and mapping (SLAM) has emerged as a solution, integrating the mobile robot's position and environmental state data from sensors to construct precise local and global maps for autonomous navigation [3]. However, this process relies on costly sensors and manual examinations to guarantee that the produced maps satisfy the required standards for path planning and localization, which may hamper its efficiency by consuming time, resources, and computational power [4,5].…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Navigation Based on Noisy N-Step Dueling Double Deep Q-Network and Prioritized Experience Replay

Hu,

Zhou,

2024

Electronics

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Effective real-time autonomous navigation for mobile robots in static and dynamic environments has become a challenging and active research topic. Although the simultaneous localization and mapping (SLAM) algorithm offers a solution, it often heavily relies on complex global and local maps, resulting in significant computational demands, slower convergence rates, and prolonged training times. In response to these challenges, this paper presents a novel algorithm called PER-n2D3QN, which integrates prioritized experience replay, a noisy network with factorized Gaussian noise, n-step learning, and a dueling structure into a double deep Q-network. This combination enhances the efficiency of experience replay, facilitates exploration, and provides more accurate Q-value estimates, thereby significantly improving the performance of autonomous navigation for mobile robots. To further bolster the stability and robustness, meaningful improvements, such as target “soft” updates and the gradient clipping mechanism, are employed. Additionally, a novel and powerful target-oriented reshaping reward function is designed to expedite learning. The proposed model is validated through extensive experiments using the robot operating system (ROS) and Gazebo simulation environment. Furthermore, to more specifically reflect the complexity of the simulation environment, this paper presents a quantitative analysis of the simulation environment. The experimental results demonstrate that PER-n2D3QN exhibits heightened accuracy, accelerated convergence rates, and enhanced robustness in both static and dynamic scenarios.

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Section: Introductionmentioning

confidence: 99%