A Lightweight and Drift-Free Fusion Strategy for Drone Autonomous and Safe Navigation

Self-localization and state estimation are crucial capabilities for agile drone autonomous navigation. This article presents a lightweight and drift-free vision-IMU-GNSS tightly coupled multisensor fusion (LDMF) strategy for drones' autonomous and safe navigation. The drone is carried out with a front-facing camera to create visual geometric constraints and generate a 3D environmental map. Ulteriorly, a GNSS receiver with multiple constellations support is used to continuously provide pseudo-range, Doppler frequency shift and UTC time pulse signals to the drone navigation system. The proposed multisensor fusion strategy leverages the Kanade-Lucas algorithm to track multiple visual features in each input image. The local graph solution is bounded in a restricted sliding window, which can immensely predigest the computational complexity in factor graph optimization procedures. The drone navigation system can achieve camera-rate performance on a small companion computer. We thoroughly experimented with the LDMF system in both simulated and real-world environments, and the results demonstrate dramatic advantages over the state-of-the-art sensor fusion strategies.