# KISS-SLAM: A Simple, Robust, and Accurate 3D LiDAR SLAM System With Enhanced Generalization Capabilities

arXiv: https://arxiv.org/abs/2503.12660 · HTML: https://mlsanity.com/paper/2503.12660/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2025.

## Built on by (most cited first, 7 of 7)

- [FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time](https://mlsanity.com/paper/2601.05738/) (2026)
- [Wanderland: Geometrically Grounded Simulation for Open-World Embodied AI](https://mlsanity.com/paper/2511.20620/) (2025)
- [SNOW: Spatio-Temporal Scene Understanding with World Knowledge for Open-World Embodied Reasoning](https://mlsanity.com/paper/2512.16461/) (2025)
- [The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset](https://mlsanity.com/paper/2606.02956/) (2026)
- [GroundLoc: Efficient Large-Scale Outdoor LiDAR-Only Localization](https://mlsanity.com/paper/2510.24623/) (2025)
- [F3DGS: Federated 3D Gaussian Splatting for Decentralized Multi-Agent World Modeling](https://mlsanity.com/paper/2604.01605/) (2026)
- [Real-Time LiDAR Gaussian Splatting SLAM](https://mlsanity.com/paper/2607.04127/) (2026)

## Abstract (arXiv)

Robust and accurate localization and mapping of an environment using laser scanners, so-called LiDAR SLAM, is essential to many robotic applications. Early 3D LiDAR SLAM methods often exploited additional information from IMU or GNSS sensors to enhance localization accuracy and mitigate drift. Later, advanced systems further improved the estimation at the cost of a higher runtime and complexity. This paper explores the limits of what can be achieved with a LiDAR-only SLAM approach while following the "Keep It Small and Simple" (KISS) principle. By leveraging this minimalistic design principle, our system, KISS-SLAM, archives state-of-the-art performances in pose accuracy while requiring little to no parameter tuning for deployment across diverse environments, sensors, and motion profiles. We follow best practices in graph-based SLAM and build upon LiDAR odometry to compute the relative motion between scans and construct local maps of the environment. To correct drift, we match local maps and optimize the trajectory in a pose graph optimization step. The experimental results demonstrate that this design achieves competitive performance while reducing complexity and reliance on additional sensor modalities. By prioritizing simplicity, this work provides a new strong baseline for LiDAR-only SLAM and a high-performing starting point for future research. Further, our pipeline builds consistent maps that can be used directly for further downstream tasks like navigation. Our open-source system operates faster than the sensor frame rate in all presented datasets and is designed for real-world scenarios.