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.

翻译：水下机器人的精确视觉惯性同时定位与建图（VI SLAM）因频繁的视觉退化及惯性测量单元（IMU）运动激励不足而仍面临重大挑战。本文提出GeVI-SLAM，一种重力增强型立体VI SLAM系统，旨在解决上述问题。通过利用立体相机的直接深度估计能力，我们消除了IMU初始化过程中对尺度估计的需求，使其即使在低加速度动态条件下也能稳定运行。借助精确的重力初始化，我们将俯仰角和横滚角从位姿估计中解耦，并求解一个4自由度（DOF）的透视n点（PnP）问题以进行位姿跟踪。这使得我们能够使用最小化的3点求解器，从而在随机采样一致性框架内显著降低计算时间以剔除异常值。我们进一步提出了一种具有可证明一致性的偏差消除4-DOF PnP估计器，确保相对位姿随特征点数量增加而收敛至真值。为处理动态运动，我们在联合估计IMU协方差的同时优化完整的6-DOF位姿，实现了对重力先验的自适应加权。在仿真和真实数据上的大量实验表明，与现有先进方法相比，GeVI-SLAM实现了更高的精度和更强的稳定性。