2019/01/11 by Tong Qin, Shaozu Cao, Qin, Tong +5 · 18 citations
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Robotic Path Planning Algorithms #Robotics and Sensor-Based Localization #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.03642
arxiv created 2019/01/11 · openalex publication_date 2019/01/11 · arxiv updated 2019/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Accurate state estimation is a fundamental problem for autonomous robots. To achieve locally accurate and globally drift-free state estimation, multiple sensors with complementary properties are usually fused together. Local sensors (camera, IMU, LiDAR, etc) provide precise pose within a small region, while global sensors (GPS, magnetometer, barometer, etc) supply noisy but globally drift-free localization in a large-scale environment. In this paper, we propose a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation. Local estimations, produced by existing VO/VIO approaches, are fused with global sensors in a pose graph optimization. Within the graph optimization, local estimations are aligned into a global coordinate. Meanwhile, the accumulated drifts are eliminated. We evaluate the performance of our system on public datasets and with real-world experiments. Results are compared against other state-of-the-art algorithms. We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization. Our implementations are open source\footnotehttps://github.com/HKUST-Aerial-Robotics/VINS-Fusion.