EVLINS: Strong Robust Navigation System Based on Event Camera

Accurate positioning and navigation capabilities are essential for Internet of Things (IoT) devices. Event cameras, inspired by biological vision sensors, exhibit robust performance in high-dynamic and low-texture environments and are particularly suitable for IoT applications. However, it faces challenges with accuracy and scale in conventional slow-motion scenarios. Conversely, light detection and ranging (LiDAR) offers high precision in normal motion conditions but degrades significantly under high-dynamic motion. To integrate the advantages of both sensors, this article introduces the EVLINS algorithm, a multisource elastic fusion method based on an extended Kalman filter (EKF). This algorithm combines event-visual-inertial odometry (EVIO), LiDAR-inertial odometry (LIO), and an inertial measurement unit (IMU), utilizing a loosely coupled trajectory layer post-processing technique. This algorithm leverages the robustness of event cameras in highly dynamic environments and the precision of LiDAR in conventional settings, utilizing normalized uncertainty and nonholonomic constraint (NHC) strategies to address LIO’s degradation and EVIO’s accuracy issues. Thorough testing in various indoor and outdoor scenarios with real-world data demonstrates that EVLINS exhibits significantly improved accuracy and robustness compared to both LIO and EVIO algorithms. In large-scale, high-dynamic outdoor environments, EVLINS achieves a 3-D position accuracy of 0.68% over 1333.58 m, improving by 33.21% over LIO and 96.10% over EVIO, which diverged mid-way. In extreme indoor dynamic scenarios, EVLINS reduces maximum position error by 41.55% compared to LIO and improves overall position accuracy by 43.48%, and 22.96% compared to EVIO.