GeVI-SLAM：面向水下机器人的重力增强型双目视觉惯性SLAM
GeVI-SLAM: Gravity-Enhanced Stereo Visua Inertial SLAM for Underwater Robots
AI总结：
针对水下机器人VI SLAM面临的视觉退化、IMU运动激励不足问题，提出重力增强的GeVI-SLAM系统，通过重力初始化解耦姿态、采用3点求解器与偏置消除4-DOF PnP估计器、自适应重力先验加权，实现了更高的定位精度与稳定性。
摘要：
由于频繁出现的视觉退化以及惯性测量单元（IMU）运动激励不足，为水下机器人实现高精度的视觉惯性同时定位与地图构建（VI SLAM）仍是一项重大挑战。本文提出了GeVI-SLAM，这是一套专为解决上述问题设计的重力增强型双目VI SLAM系统。借助双目相机的直接深度估计能力，我们省去了IMU初始化过程中的尺度估计步骤，使系统即便在低加速度动态工况下也能稳定运行。通过精确的重力初始化，我们将俯仰角和滚转角从位姿估计中解耦，通过求解4自由度（DOF）的Perspective-n-Point（PnP，即透视n点）问题完成位姿跟踪。这使得我们可以采用最小化的3点求解器，在随机抽样一致性（Random Sample Consensus）框架下大幅降低外点剔除的计算耗时。我们进一步提出了一种具备可证明一致性的偏置消除型4自由度PnP估计器，确保相对位姿会随着特征点数量增加收敛至真实值。为应对动态运动场景，我们在联合估计IMU协方差的同时优化完整6自由度位姿，实现对重力先验的自适应加权。在模拟数据与真实世界数据上开展的大量实验表明，与现有最优方法相比，GeVI-SLAM具备更高的精度与更强的稳定性。
英文摘要：
Accurate visual inertial simultaneous localization and mapping (VI SLAM) for underwater robots remains a significant challenge due to frequent visual degeneracy and insufficient inertial measurement unit (IMU) motion excitation. In this paper, we present GeVI-SLAM, a gravity-enhanced stereo VI SLAM system designed to address these issues. By leveraging the stereo camera's direct depth estimation ability, we eliminate the need to estimate scale during IMU initialization, enabling stable operation even under low acceleration dynamics. With precise gravity initialization, we decouple the pitch and roll from the pose estimation and solve a 4 degrees of freedom (DOF) Perspective-n-Point (PnP) problem for pose tracking. This allows the use of a minimal 3-point solver, which significantly reduces computational time to reject outliers within a Random Sample Consensus framework. We further propose a bias-eliminated 4-DOF PnP estimator with provable consistency, ensuring the relative pose converges to the true value as the feature number increases. To handle dynamic motion, we refine the full 6-DOF pose while jointly estimating the IMU covariance, enabling adaptive weighting of the gravity prior. Extensive experiments on simulated and real-world data demonstrate that GeVI-SLAM achieves higher accuracy and greater stability compared to state-of-the-art methods.
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