A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
Baichuan Huang, Jun Zhao, Jingbin Liu

TL;DR
This survey comprehensively reviews SLAM technologies, including Lidar, visual, and fused approaches, and discusses future challenges and opportunities in 6G wireless networks for SLAM applications.
Contribution
It provides a detailed, high-quality overview of SLAM methods, sensor types, and fusion techniques, serving as a resource for both newcomers and experienced researchers.
Findings
Overview of Lidar and visual SLAM systems and sensors
Discussion on deep learning integration in SLAM
Future challenges and open questions in 6G networks
Abstract
Simultaneous Localization and Mapping (SLAM) achieves the purpose of simultaneous positioning and map construction based on self-perception. The paper makes an overview in SLAM including Lidar SLAM, visual SLAM, and their fusion. For Lidar or visual SLAM, the survey illustrates the basic type and product of sensors, open source system in sort and history, deep learning embedded, the challenge and future. Additionally, visual inertial odometry is supplemented. For Lidar and visual fused SLAM, the paper highlights the multi-sensors calibration, the fusion in hardware, data, task layer. The open question and forward thinking with an envision in 6G wireless networks end the paper. The contributions of this paper can be summarized as follows: the paper provides a high quality and full-scale overview in SLAM. It's very friendly for new researchers to hold the development of SLAM and learn it…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRobotics and Sensor-Based Localization · Indoor and Outdoor Localization Technologies · Robotic Path Planning Algorithms
