Simultaneous Localization and Mapping (SLAM) for Autonomous Driving: Concept and Analysis

Abstract

The Simultaneous Localization and Mapping (SLAM) technique has achieved astonishing progress over the last few decades and has generated considerable interest in the autonomous driving community. With its conceptual roots in navigation and mapping, SLAM outperforms some traditional positioning and localization techniques since it can support more reliable and robust localization, planning, and controlling to meet some key criteria for autonomous driving. In this study the authors first give an overview of the different SLAM implementation approaches and then discuss the applications of SLAM for autonomous driving with respect to different driving scenarios, vehicle system components and the characteristics of the SLAM approaches. The authors then discuss some challenging issues and current solutions when applying SLAM for autonomous driving. Some quantitative quality analysis means to evaluate the characteristics and performance of SLAM systems and to monitor the risk in SLAM estimation are reviewed. In addition, this study describes a real-world road test to demonstrate a multi-sensor-based modernized SLAM procedure for autonomous driving. The numerical results show that a high-precision 3D point cloud map can be generated by the SLAM procedure with the integration of Lidar and GNSS/INS. Online four–five cm accuracy localization solution can be achieved based on this pre-generated map and online Lidar scan matching with a tightly fused inertial system.

Simulation Analysis of Fire-Fighting Path Planning Based On SLAM

“…By determining the coordinates of objects in the surrounding environment, it is possible to generate a map. This process is referred to as mapping [6]. RTAB-Map fuses feature points and depth image data to build an incremental map of the environment.…”

“…Bayesian filtering where all variables are treated as Gaussian random variables. includes two steps: time update (prediction) and measurement update (filtering) [9]. The Bayesian filter is a probability-based filtering technique that utilizes Bayesian theorem and is commonly used for state estimation and prediction problems.…”

A review of visual SLAM for robotics: evolution, properties, and future applications

“…The imperative of integrating automation with human cognitive abilities becomes evident in facilitating a successful collaboration between humans and robots. This helps service robots be more effective in different situations where they interact with people ( Prati et al, 2021 ; Strazdas et al, 2020 ; Zheng et al, 2023 ). Furthermore, using multiple robots together can help them handle complex tasks better ( Zheng et al, 2022 ; Li et al, 2023b ; Fiedler et al, 2021 ).…”

Indoor Mapping with Entertainment Devices: Evaluating the Impact of Different Mapping Strategies for Microsoft HoloLens 2 and Apple iPhone 14 Pro

“…Since then, TLS has been further expanded with the maturity of mobile laser scanning (MLS) technology and simultaneous localization and mapping (SLAM) [ 4 , 5 ]. MLS systems incorporate laser scanners on mobile platforms such as vehicles [ 6 , 7 , 8 ], drones [ 9 , 10 , 11 ], and backpacks [ 12 , 13 ], allowing them to capture data in indoor and outdoor environments while moving. At the same time, the use of SLAM technology allows the MLS system to locate and build maps in real time in unknown environments, no longer limited by the mapping scope of static sensors.…”