Laser–visual–inertial odometry and mapping with high robustness and low drift

TLDR

The paper proposes a data‑processing pipeline that fuses 3D laser, camera, and IMU data to estimate ego‑motion and build a map online. The method employs a sequential, multilayer pipeline that first predicts motion with IMU mechanization, then refines it with visual‑inertial coupling, and finally applies scan matching, while automatically reconfiguring to bypass failed modules. Experiments demonstrate high‑frequency, low‑latency ego‑motion estimation, dense accurate 3D mapping, 0.22% drift over 9.3 km, and robustness to dynamic motion, low‑light, texture‑less, and high‑speed driving up to 33 m/s.

Abstract

Abstract We present a data processing pipeline to online estimate ego‐motion and build a map of the traversed environment, leveraging data from a 3D laser scanner, a camera, and an inertial measurement unit (IMU). Different from traditional methods that use a Kalman filter or factor‐graph optimization, the proposed method employs a sequential, multilayer processing pipeline, solving for motion from coarse to fine. Starting with IMU mechanization for motion prediction, a visual–inertial coupled method estimates motion; then, a scan matching method further refines the motion estimates and registers maps. The resulting system enables high‐frequency, low‐latency ego‐motion estimation, along with dense, accurate 3D map registration. Further, the method is capable of handling sensor degradation by automatic reconfiguration bypassing failure modules. Therefore, it can operate in the presence of highly dynamic motion as well as in the dark, texture‐less, and structure‐less environments. During experiments, the method demonstrates 0.22% of relative position drift over 9.3 km of navigation and robustness w.r.t. running, jumping, and even highway speed driving (up to 33 m/s).