A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

By César Debeunne, Damien Vivet. Italian National Conference on Sensors, 2020

344 citations recorded by the source corpus.

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/1b4741c9e29d445c1a7b0e4c3300b81793e26dbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Fu Zhang
2. Jiarong Lin
3. Chen Wang
4. Lihua Xie
5. Han Wang
6. Daniela Rus
7. Carlo Ratti
8. Chun-Lin Chen

In Computer Vision and Pattern Recognition

1. Chen Wang
2. Fu Zhang
3. Jiarong Lin
4. Lihua Xie
5. Han Wang
6. Daniela Rus
7. Carlo Ratti
8. Chun-Lin Chen

In Ocean Engineering

1. Daniela Rus
2. Fu Zhang

Citations in the corpus

• LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping (2021, background): “Recently, lidar-visual-inertial systems have attracted increased attention due to their robustness in sensor degraded tasks [15].”
• R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package (2021, background): “In particular, various of LiDAR-Visual fusion frameworks have been proposed recently in the robotics community [18].”
• Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, unknown): “[20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], static object detection [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], dynamic object detection [41], [42], [43], [44],”
• F-LOAM : Fast LiDAR Odometry and Mapping (2021, background): “Compared to visual SLAM, LiDAR SLAM is often more accurate in pose estimation and is robust to environmental variations such as illumination and weather change [1].”