A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks

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 very obviously. Also, the paper can be considered as a dictionary for experienced researchers to search and find new interesting orientation.