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

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.