Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks as it tackles simultaneous mapping of the unknown environment defined by multiple landmark positions and localization of the unknown pose (i.e., attitude and position) of the robot in three-dimensional (3D) space. The true SLAM problem is modeled on the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, and its true dynamics rely on angular and translational velocities. This paper proposes a novel geometric nonlinear stochastic estimator algorithm for SLAM on $\mathbb{SLAM}_{n}\left(3\right)$ that precisely mimics the nonlinear motion dynamics of the true SLAM problem. Unlike existing solutions, the proposed stochastic filter takes into account unknown constant bias and noise attached to the velocity measurements. The proposed nonlinear stochastic estimator on manifold is guaranteed to produce good results provided with the measurements of angular velocities, translational velocities, landmarks, and inertial measurement unit (IMU). Simulation and experimental results reflect the ability of the proposed filter to successfully estimate the six-degrees-of-freedom (6 DoF) robot's pose and landmark positions. Keywords: Simultaneous Localization and Mapping, nonlinear stochastic observer for SLAM, stochastic differential equations, pose estimator, position, attitude, Brownian motion process, inertial measurement unit, landmarks, features, SDE, SO(3), SE(3), SLAM.
翻译:同步本地化和映射( SLAM) 是关键的机器人任务之一, 因为它处理由多个里程碑位置界定的未知环境同时绘图, 以及机器人在三维( 3D) 空间的未知面( 态度和位置) 。 真正的 SLAM 问题建模在 $\ mathb{ SLAM ⁇ n ⁇ left( 3\ right) 的 Lie Group 上, 其真实动态依赖于角速和翻译速度。 本文建议为 SLAM 提供一个新的地球测量非线性非直线性估测器算法, 以 $\ mathb{ Slathb{ sign( left( 3\ right) ) 定位为定义, 精确模拟真正的 SLISM 问题的非线性运动动态。 与现有解决方案不同, 拟议的随机过滤器考虑到未知的恒定偏差和噪音。 文中拟议的非线性直线性测测测算符, 保证产生良好的结果, 包括角速度测算、 速度测算、 平等距定的Slexcial- salideal- salial 度、 rodealtialalalal- salialalalalalalalalalalal 度 度测算器、 6 和Slimal- sal- sal- salibal- saltial- estal- sal- sal- saltialdal- sal- sal- sal- salmalmaldaldaldal- sal- saldaldaldal- estal- saldal- sal- sal- sal- sal- sal- saldal- sal- sal- saldaldaldal- sal- sal- sal- sal- sal- saldaldaldaldalbalal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- sal- saldaldal- sal- sal- saldal- sal- sal- sal- sal