Navigation solutions suitable for cases when both autonomous robot's pose (\textit{i.e}., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by concurrently mapping the environment and observing robot's pose with respect to the map. This work proposes a nonlinear observer for SLAM posed on the manifold of the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, characterized by systematic convergence, and designed to mimic the nonlinear motion dynamics of the true SLAM problem. The system error is constrained to start within a known large set and decay systematically to settle within a known small set. The proposed estimator is guaranteed to achieve predefined transient and steady-state performance and eliminate the unknown bias inevitably present in velocity measurements by directly using measurements of angular and translational velocity, landmarks, and information collected by an inertial measurement unit (IMU). Experimental results obtained by testing the proposed solution on a real-world dataset collected by a quadrotor demonstrate the observer's ability to estimate the six-degrees-of-freedom (6 DoF) robot pose and to position unknown landmarks in three-dimensional (3D) space. Keywords: Simultaneous Localization and Mapping, Nonlinear filter for SLAM, Nonlinear filter for SLAM on Matrix Lie group, pose, asymptotic stability, prescribed performance, adaptive estimate, feature, inertial measurement unit, inertial vision unit, IMU, SE(3), SO(3), noise.

中文翻译：

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具有里程碑意义和IMU数据融合：SLAM和速度偏差的系统收敛几何非线性观测器

迫切需要适用于自主机器人的姿势（\ textit {ie}。，姿态和位置）及其环境都不明的情况的导航解决方案。同步定位和映射（SLAM）通过同时映射环境并观察机器人相对于地图的姿态来满足此需求。这项工作提出了SLAM的非线性观测器，该观测器置于$ \ mathbb {SLAM} _ {n} \ left（3 \ right）$的Lie群的流形上，具有系统收敛性，旨在模拟非线性运动动力学。真正的SLAM问题。系统误差被约束为在已知的大集合内开始，并且系统地衰减以稳定在已知的小集合内。通过直接使用角速度和平移速度，界标和惯性测量单元（IMU）收集的信息，可以保证所提出的估算器达到预定的瞬态和稳态性能，并消除速度测量中不可避免地存在的未知偏差。通过在由四旋翼飞机收集的真实世界数据集上测试所提出的解决方案而获得的实验结果表明，观察者能够估算六自由度（6 DoF）机器人的姿态，并能够在三维（3D）中定位未知地标空间。关键字：同时定位和映射，SLAM非线性滤波器，Matrix Lie组上SLAM非线性滤波器，姿势，渐近稳定性，规定性能，自适应估计，特征，惯性测量单位，惯性视觉单位，IMU，SE（3），