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Electronics
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Simultaneous Localization and Mapping (SLAM) of Mobile Robots
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Simultaneous Localization and Mapping (SLAM) of Mobile Robots

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 October 2025 | Viewed by 670

Special Issue Editors

Dr. Zhan Wang

E-Mail Website
Guest Editor
1. Zhejiang Energy Digital Technology Co., Ltd., Hangzhou 310012, China
2. School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
Interests: SLAM; localization & navigation; multi-sensor fusion; deep learning; computer vision; autonomous system
Dr. Jin Wang

E-Mail Website
Guest Editor
State Key Laboratory of Fluid Power, School of Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Interests: SLAM; computer vision; path planning; autonomous system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging technology in enabling mobile robots to operate autonomously in unknown environments, where safety and precision are the vital issues for reliable and high-level autonomous applications. This Special Issue seeks to gather insights into state-of-the-art research and to promote the continuous progress of SLAM technology for mobile robots. Topics of interest for this Special Issue include, but are not limited to, the following:

  1. Advanced SLAM Algorithm Development: The improvement of existing algorithms to increase the accuracy and robustness of localization and mapping.
  2. AI and Machine Learning for SLAM: The integration of advanced AI and machine learning techniques with SLAM algorithms in dynamic and complex environments.
  3. Expansion of Application Scenarios: Applications of SLAM technology in new scenarios, such as environmental monitoring, autonomous delivery, and cultural heritage exploration.
  4. Cooperative SLAM (C-SLAM): Multi-robot SLAM system development, dealing with communication barriers, co-localization issues, and joint optimization problems to enable efficient and effective multi-robot navigation.
  5. Fast SLAM for Edge Deployment: Real-time SLAM on edge devices, Hardware–Software co-design optimization, efficient data processing technologies, and resource-constrained SLAM solutions.

Dr. Zhan Wang
Dr. Jin Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • localization and mapping
  • robotic navigation
  • autonomous system
  • AI-enhanced SLAM
  • vision inertial SLAM
  • multi-sensor fusion
  • mobile robot

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Published Papers (2 papers)

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Research

24 pages, 988 KiB  
Article
Consistency-Oriented SLAM Approach: Theoretical Proof and Numerical Validation
by Zhan Wang, Alain Lambert, Yuwei Meng, Rongdong Yu, Jin Wang and Wei Wang
Electronics 2025, 14(15), 2966; https://doi.org/10.3390/electronics14152966 - 24 Jul 2025
Viewed by 204
Abstract
Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging task in robotics literature, where safety and reliability are the critical issues for successfully autonomous applications of robots. Classically, the SLAM problem is tackled via probabilistic or optimization methods (such as [...] Read more.
Simultaneous Localization and Mapping (SLAM) has long been a fundamental and challenging task in robotics literature, where safety and reliability are the critical issues for successfully autonomous applications of robots. Classically, the SLAM problem is tackled via probabilistic or optimization methods (such as EKF-SLAM, Fast-SLAM, and Graph-SLAM). Despite their strong performance in real-world scenarios, these methods may exhibit inconsistency, which is caused by the inherent characteristic of model linearization or Gaussian noise assumption. In this paper, we propose an alternative monocular SLAM algorithm which theoretically relies on interval analysis (iMonoSLAM), to pursue guaranteed rather than probabilistically defined solutions. We consistently modeled and initialized the SLAM problem with a bounded-error parametric model. The state estimation process is then cast into an Interval Constraint Satisfaction Problem (ICSP) and resolved through interval constraint propagation techniques without any linearization or Gaussian noise assumption. Furthermore, we theoretically prove the obtained consistency and propose a versatile method for numerical validation. To the best of our knowledge, this is the first time such a proof has been proposed. A plethora of numerical experiments are carried to validate the consistency, and a preliminary comparison with classical EKF-SLAM in different noisy situations is also presented. Our proposed iMonoSLAM shows outstanding performance in obtaining reliable solutions, highlighting the potential application prospect in safety-critical scenarios of mobile robots. Full article
(This article belongs to the Special Issue Simultaneous Localization and Mapping (SLAM) of Mobile Robots)
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19 pages, 2016 KiB  
Article
A Robust and Energy-Efficient Control Policy for Autonomous Vehicles with Auxiliary Tasks
by Yabin Xu, Chenglin Yang and Xiaoxi Gong
Electronics 2025, 14(15), 2919; https://doi.org/10.3390/electronics14152919 - 22 Jul 2025
Viewed by 249
Abstract
We present a lightweight autonomous driving method that uses a low-cost camera, a simple end-to-end convolutional neural network architecture, and smoother driving techniques to achieve energy-efficient vehicle control. Instead of directly constructing a mapping from raw sensory input to the action, our network [...] Read more.
We present a lightweight autonomous driving method that uses a low-cost camera, a simple end-to-end convolutional neural network architecture, and smoother driving techniques to achieve energy-efficient vehicle control. Instead of directly constructing a mapping from raw sensory input to the action, our network takes the frame-to-frame visual difference as one of the crucial inputs to produce control commands, including the steering angle and the speed value at each time step. This choice of input allows highlighting the most relevant parts on raw image pairs to decrease the unnecessary visual complexity caused by different road and weather conditions. Additionally, our network achieves the prediction of the vehicle’s upcoming control commands by incorporating a view synthesis component into the model. The view synthesis, as an auxiliary task, aims to infer a novel view for the future from the historical environment transformation cue. By combining both the current and upcoming control commands, our framework achieves driving smoothness, which is highly associated with energy efficiency. We perform experiments on benchmarks to evaluate the reliability under different driving conditions in terms of control accuracy. We deploy a mobile robot outdoors to evaluate the power consumption of different control policies. The quantitative results demonstrate that our method can achieve energy efficiency in the real world. Full article
(This article belongs to the Special Issue Simultaneous Localization and Mapping (SLAM) of Mobile Robots)
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