GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multisensor Fused Odometry With Gaussian Mapping

Abstract

In recent years, 3D Gaussian splatting (3D-GS) has emerged as a novel scene representation approach. However, existing vision-only 3D-GS methods often rely on hand-crafted heuristics for point-cloud densification and face challenges in handling occlusions and high GPU memory and computation consumption [1]. LiDAR-Inertial-Visual (LIV) sensor configuration has demonstrated superior performance in precise localization and dense mapping by leveraging complementary sensing characteristics: rich texture information from cameras, precise geometric measurements from LiDAR, and high-frequency motion data from IMU [2]-[8]. Inspired by this, we propose a novel real-time Gaussian-based simultaneous localization and mapping (SLAM) system. Our map system comprises a global Gaussian map and a sliding window of Gaussians, along with an IESKF-based real-time odometry utilizing Gaussian maps. The structure of the global Gaussian map consists of hash-indexed voxels organized in a recursive octree. This hierarchical structure effectively covers sparse spatial volumes while adapting to different levels of detail and scales in the environment. The Gaussian map is efficiently initialized through multi-sensor fusion and optimized with photometric gradients. Our system incrementally maintains a sliding window of Gaussians with minimal graphics memory usage, significantly reducing GPU computation and memory consumption by only optimizing the map within the sliding window, enabling real-time optimization. Moreover, we implement a tightly coupled multi-sensor fusion odometry with an iterative error state Kalman filter (IESKF), which leverages real-time updating and rendering of the Gaussian map to achieve competitive localization accuracy. Our system represents the first real-time Gaussian-based SLAM framework deployable on resource-constrained embedded systems (all implemented in C++/CUDA for efficiency), demonstrated on the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NVIDIA Jetson Orin NX</b> platform. The framework achieves real-time performance while maintaining robust multi-sensor fusion capabilities. All implementation algorithms, hardware designs, and CAD models and demo video of our GPU-accelerated system will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HKUST-Aerial-Robotics/GS-LIVO</uri>.