MAVIS: SE2(3)に基づく厳密なIMU事前積分を用いたマルチカメラ拡張視覚慣性SLAM
MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration
複数の部分的に重なるカメラとIMUを統合し、SE2(3)の自己同型写像の指数関数に基づく改良IMU事前積分を導入した最適化ベースの視覚慣性SLAMシステムを提案。Hilti SLAM Challenge 2023の全視覚-IMUトラックで1位を獲得。
著者: Yifu Wang, Yonhon Ng, Inkyu Sa, Alvaro Parra, Cristian Rodriguez, Tao Jun Lin, Hongdong Li
分類: cs.RO
原文アブストラクト
We present a novel optimization-based Visual-Inertial SLAM system designed for multiple partially overlapped camera systems, named MAVIS. Our framework fully exploits the benefits of wide field-of-view from multi-camera systems, and the metric scale measurements provided by an inertial measurement unit (IMU). We introduce an improved IMU pre-integration formulation based on the exponential function of an automorphism of SE_2(3), which can effectively enhance tracking performance under fast rotational motion and extended integration time. Furthermore, we extend conventional front-end tracking and back-end optimization module designed for monocular or stereo setup towards multi-camera systems, and introduce implementation details that contribute to the performance of our system in challenging scenarios. The practical validity of our approach is supported by our experiments on public datasets. Our MAVIS won the first place in all the vision-IMU tracks (single and multi-session SLAM) on Hilti SLAM Challenge 2023 with 1.7 times the score compared to the second place.
