MFVINS：基于多鱼眼相机的视觉惯性系统
MFVINS: Multiple Fisheye Camera-Based Visual Inertial System
- Jeonbuk National University（国立全北大学）
- BSTAR Robotics, Inc.（BSTAR机器人公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对单目VINS在遮挡、光照变化和无纹理环境下误差累积的问题，提出基于多鱼眼相机的MFVINS，采用IMU辅助FAST特征跟踪、畸变外点滤除及带物理约束的重投影误差束调整，实现实时鲁棒定位。
AI中文摘要：
使用单目相机和低成本惯性测量单元（IMU）传感器的同时定位与建图（SLAM）方法是实现低成本传感器配置的有效途径。采用这种传感器配置时，视觉惯性系统（VINS）专注于融合相机和IMU传感器的数据，以估计传感器姿态的六自由度（DOF）。通常，VINS仅使用单个相机作为视觉输入，这会导致因遮挡、各种光照条件以及无纹理环境而产生误差累积等问题。本文提出了一种新的基于多鱼眼相机的视觉惯性系统，称为MFVINS。我们提出了一种面向多相机的IMU辅助FAST特征跟踪器，能够高效提取并稳健匹配局部特征。然后，所提方法在归一化图像平面上滤除由鱼眼畸变引起的外点。随后，提出了一种具有物理有效性约束的新型重投影误差，用于基于学习深度估计的束调整。所提方法被应用于多种场景，并通过与先前VINS方法的比较证明了其有效性。特别地，MFVINS以实时进程实现，以利用多相机在遮挡和无纹理区域鲁棒性方面的优势，同时降低计算负担。
英文摘要：
A simultaneous localization and mapping (SLAM) method using a monocular camera and a low-cost inertial measurement unit (IMU) sensor is an effective way to fulfill a low-cost sensor configuration. Using this sensor configuration, visual-inertial system (VINS) focuses on fusing data from a camera and an IMU sensor to estimate the six degrees-of-freedom (DOF) of the sensor pose. Typically, VINS uses only a single camera as visual input, which lead to problems such as error accumulation due to occlusion, various illumination, and textureless environments. In this paper, we propose a new multiple fisheye camera-based visual-inertial system called MFVINS. We present an IMU-aided FAST feature tracker for multiple cameras that enables efficient extraction and robust matching of local features. Then, the proposed method filters out outliers caused by fisheye distortion on the normalized image plane. Subsequently, a new reprojection error with physical validity constraints is proposed for bundle adjustment using learning-based depth estimation. The proposed method is applied to various scenarios, and its effectiveness is demonstrated by comparing previous VINS methods. In particular, MFVINS is implemented in real-time process to leverage the advantages of using multiple cameras -- robustness against occlusion and textureless regions -- while reducing the computational burden.
