Awesome-LiDAR-Visual-SLAM
A curated list of top-tier publications and resources for LiDAR-Visual fusion SLAM systems.
What is Awesome-LiDAR-Visual-SLAM?
Awesome-LiDAR-Visual-SLAM is a curated GitHub repository listing academic papers, code, and datasets related to SLAM systems that fuse LiDAR and visual sensors. It addresses the problem of researchers and developers needing to manually survey a fragmented landscape of publications by providing a centralized, updated overview of state-of-the-art fusion algorithms.
Researchers, graduate students, and engineers working in robotics, autonomous vehicles, and embodied AI who are specifically focused on developing or implementing robust LiDAR-Visual SLAM systems.
Developers choose this resource because it saves significant literature review time by aggregating high-quality, peer-reviewed resources in one place. Its focus on fusion SLAM and active maintenance by an expert in the field ensures relevance and comprehensiveness.
Overview
A curated list of resources relevant to LiDAR-Visual-Fusion-SLAM
Use Cases
Best For
- Finding the latest research papers on LiDAR-Visual-Inertial odometry
Related Projects
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