How to get started with LiDAR-visual SLAM using this awesome list?

Start by browsing recent papers like R3LIVE or FAST-LIVO2 from the 2024-2025 entries, then clone their linked GitHub repositories for code. Use provided datasets, such as M3DGR, to test algorithms in simulated environments first.

What are the best datasets for benchmarking LiDAR-visual SLAM systems?

The list includes benchmarking resources like the M3DGR dataset from the 2025 paper. Check papers with dataset links, such as those in 2024 entries, for evaluating performance in challenging conditions like underground or dynamic scenes.

LiDAR-visual SLAM or pure visual SLAM for indoor robotics?

LiDAR-visual SLAM is generally more accurate and robust in textureless or low-light indoor environments due to LiDAR's depth data, but requires costlier hardware. Refer to papers like LVI-SAM for specific indoor performance comparisons.

How to contribute my LiDAR-visual SLAM research to this list?

Submit a pull request on GitHub with your paper and code links, as per the README's contribution instructions. Ensure your work is accepted to top conferences like IROS or ICRA to align with the curation standards.

What's the difference between R3LIVE and LVI-SAM for real-time applications?

R3LIVE emphasizes RGB-colored, real-time state estimation with tight coupling, while LVI-SAM focuses on smoothing and mapping in dynamic environments. Review their respective papers and codebases for latency and accuracy trade-offs.

Open-Awesome

Awesome-LiDAR-Visual-SLAM

MIT

A curated list of top-tier publications and resources for LiDAR-Visual fusion SLAM systems.

GitHub

309 stars28 forks0 contributors

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.

Target Audience

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.

Value Proposition

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
Discovering open-source code implementations for multi-sensor SLAM
Surveying the evolution of sensor fusion techniques in SLAM from 2015 onward
Identifying benchmark datasets for evaluating SLAM in challenging environments
Contributing your own LiDAR-vision fusion SLAM algorithm to a curated list
Staying updated on top conference publications (IROS, ICRA, RAL) in this niche

Not Ideal For

Engineers needing production-ready SLAM software with commercial support and documentation
Projects focused on low-cost, camera-only SLAM without LiDAR hardware
Beginners seeking introductory tutorials or step-by-step guides on SLAM fundamentals

Pros & Cons

Pros

Extensive Publication Coverage

Curates over 30 top-tier papers from 2015 to 2025, with direct links to papers and code, as seen in the detailed yearly lists including IROS, ICRA, and RAL publications.

Community-Driven Updates

Actively encourages contributions via pull requests, ensuring the list stays current with latest research, as highlighted in the README's contribution section and 2025 updates.

Fusion-Specific Focus

Dedicated exclusively to LiDAR-visual fusion SLAM, leveraging precise LiDAR and rich visual data for robust performance, per the introduction and multi-sensor fusion emphasis.

Cons

No Implementation Guidance

Only provides paper and code links without installation instructions, troubleshooting, or integration advice, leaving users to navigate complex setups independently.

Academic-Only Scope

Focuses solely on peer-reviewed publications, missing industry-grade tools and proprietary solutions that might be more relevant for commercial deployments.

Limited Critical Analysis

Lists papers chronologically without summaries, comparisons, or performance benchmarks, requiring users to conduct their own evaluations of algorithm suitability.

Frequently Asked Questions

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