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DEVELOPING MOVING HORIZON ESTIMATION BASED RANGING MEASUREMENT FOR SUPPORTING VISION-AIDED INERTIAL NAVIGATION SYSTEM

机译:开发基于视界估计的移动视距,以支持视觉辅助惯性导航系统

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The objective of this paper is to develop an advanced Vision-and-Ranging-aided Inertial Navigation System (VRINS), which combines a Vision-aided Inertial Navigation System (VINS) with Moving Horizon Estimation (MHE) based ranging measurement update. The traditional VINS estimate suffers the error accumulation from the camera observation, which makes the system diverge and fails to track the vehicle trajectory in long-term operation. Hence, a ranging sensor is integrated with VINS in the sequential-sensor-update structure, which allows the filter to operate for longer duration. The ranging measurement update is developed with the MHE, which directly incorporates the system constraints into the optimization process. The VINS is developed with Cubature Multi-State Constraint Kalman Filter (MSCKF), which has 30-dimension filter state, tight constraints of state transition and observability. Those elements need to be considered in the design of MHE optimization. The implementation of MHE is conducted with CASADI library. The proposed VRINS will be validated using KITTI dataset and compared against the VINS.
机译:本文的目的是开发一种先进的视觉与范围辅助惯性导航系统(VRINS),该系统将视觉辅助惯性导航系统(VINS)与基于移动视点估计(MHE)的测距测量更新相结合。传统的VINS估计会受到相机观察到的误差累积的影响,这会使系统发散,并且无法在长期运行中跟踪车辆的轨迹。因此,在顺序传感器更新结构中,测距传感器与VINS集成在一起,从而允许滤波器运行更长的时间。测距测量更新是通过MHE开发的,该MHE将系统约束直接纳入优化过程。 VINS是使用Cubature多状态约束卡尔曼滤波器(MSCKF)开发的,该滤波器具有30维滤波器状态,严格的状态转换约束和可观察性。在MHE优化设计中需要考虑这些要素。 MHE的实施是通过CASADI库进行的。建议的VRINS将使用KITTI数据集进行验证,并与VINS进行比较。

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