SLAM and Adaptive Navigation for Robotics

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Sensors and Control in Robotics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 891

Special Issue Editors

College of Intelligent System Science and Engineering, Harbin Engineering University, Qingdao 266400, China
Interests: path planning for unmanned systems; full coverage path planning; task allocation; SLAM, ROS, path planning algorithms based on artificial intelligence and swarm intelligence, etc.
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Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
Interests: inverse kinematics; PSO algorithm; BP neural network; precise localization; puncturing robot

Special Issue Information

Dear Colleagues,

Simultaneous Localization and Mapping (SLAM) and adaptive navigation are fundamental to enabling diverse autonomous robotic systems—including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned surface vehicles (USVs), and unmanned underwater vehicles (UUVs)—to perceive, localize, and coordinate effectively in complex and dynamic environments. This Special Issue explores the integration of SLAM and adaptive navigation techniques for single-robot systems, multi-robot systems, and large-scale robotic swarms. Topics of interest include, but are not limited to, SLAM, localization and mapping, adaptive navigation, and path planning. We especially encourage submissions focusing on swarm coordination strategies such as cluster-wide coverage path planning, distributed task allocation, and dynamic topology control. Contributions that harness artificial intelligence, machine learning, and bio-inspired algorithms to improve robustness, scalability, and real-time adaptability of heterogeneous robotic systems are highly welcome. This topic aligns closely with the scope of Robotics by advancing coordinated autonomy and intelligent navigation for robots operating in unstructured and unpredictable environments.

Dr. Jinjin Yan
Dr. Minzhou Luo
Guest Editors

Manuscript Submission Information

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Keywords

  • SLAM
  • adaptive navigation
  • multi-robot systems
  • swarm robotics
  • UAVs
  • USVs
  • UUVs
  • localization
  • path planning
  • swarm intelligence
  • control

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Published Papers (1 paper)

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Research

28 pages, 10315 KB  
Article
DKB-SLAM: Dynamic RGB-D Visual SLAM with Efficient Keyframe Selection and Local Bundle Adjustment
by Qian Sun, Ziqiang Xu, Yibing Li, Yidan Zhang and Fang Ye
Robotics 2025, 14(10), 134; https://doi.org/10.3390/robotics14100134 - 25 Sep 2025
Viewed by 596
Abstract
Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel in static settings, issues like keyframe redundancy and optimization inefficiencies further hinder [...] Read more.
Reliable navigation for mobile robots in dynamic, human-populated environments remains a significant challenge, as moving objects often cause localization drift and map corruption. While Simultaneous Localization and Mapping (SLAM) techniques excel in static settings, issues like keyframe redundancy and optimization inefficiencies further hinder their practical deployment on robotic platforms. To address these challenges, we propose DKB-SLAM, a real-time RGB-D visual SLAM system specifically designed to enhance robotic autonomy in complex dynamic scenes. DKB-SLAM integrates optical flow with Gaussian-based depth distribution analysis within YOLO detection frames to efficiently filter dynamic points, crucial for maintaining accurate pose estimates for the robot. An adaptive keyframe selection strategy balances map density and information integrity using a sliding window, considering the robot’s motion dynamics through parallax, visibility, and matching quality. Furthermore, a heterogeneously weighted local bundle adjustment (BA) method leverages map point geometry, assigning higher weights to stable edge points to refine the robot’s trajectory. Evaluations on the TUM RGB-D benchmark and, crucially, on a mobile robot platform in real-world dynamic scenarios, demonstrate that DKB-SLAM outperforms state-of-the-art methods, providing a robust and efficient solution for high-precision robot localization and mapping in dynamic environments. Full article
(This article belongs to the Special Issue SLAM and Adaptive Navigation for Robotics)
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