传感器查询计划与传感器噪声协方差用于精度约束的轨迹估计
Sensor Query Schedule and Sensor Noise Covariances for Accuracy-constrained Trajectory Estimation
AI总结：
本文提出了一种方法，用于估计传感器查询计划和噪声协方差，以实现特定的轨迹估计精度，通过半正定规划解决，并在仿真和真实实验中验证了其有效性。
摘要：
轨迹估计涉及通过结合关于移动机器人动态模型的先验知识与通过传感器获得的噪声状态观测来确定机器人的轨迹。此类过程的准确性由系统模型的保真度和传感器参数决定，例如传感器的精度（由其噪声协方差表示）以及它能够生成观测的速率，称为传感器查询计划。直观上，高频率的准确传感器测量会导致准确的轨迹估计。然而，成本和资源限制限制了传感器的精度及其测量速率。我们的工作的新贡献是估计实现特定估计精度所需的传感器计划和传感器协方差。具体而言，我们关注估计：（i）已知协方差的传感器必须以多快的速率生成测量以实现特定的估计精度，或者（ii）对于给定的传感器更新速率，实现特定估计精度所需的传感器协方差。我们将估计这些传感器参数的问题公式化为半正定规划问题，这可以通过现成的求解器解决。我们通过在仿真和真实实验中展示，使用我们提出的方法计算出的传感器计划和传感器协方差能够实现所需的轨迹估计精度。我们的方法还识别出在给定的系统和传感器特性下某些估计精度无法实现的场景。
英文摘要：
Trajectory estimation involves determining the trajectory of a mobile robot by combining prior knowledge about its dynamic model with noisy observations of its state obtained using sensors. The accuracy of such a procedure is dictated by the system model fidelity and the sensor parameters, such as the accuracy of the sensor (as represented by its noise covariance) and the rate at which it can generate observations, referred to as the sensor query schedule. Intuitively, high-rate measurements from accurate sensors lead to accurate trajectory estimation. However, cost and resource constraints limit the sensor accuracy and its measurement rate. Our work's novel contribution is the estimation of sensor schedules and sensor covariances necessary to achieve a specific estimation accuracy. Concretely, we focus on estimating: (i) the rate or schedule with which a sensor of known covariance must generate measurements to achieve specific estimation accuracy, and alternatively, (ii) the sensor covariance necessary to achieve specific estimation accuracy for a given sensor update rate. We formulate the problem of estimating these sensor parameters as semidefinite programs, which can be solved by off-the-shelf solvers. We validate our approach in simulation and real experiments by showing that the sensor schedules and the sensor covariances calculated using our proposed method achieve the desired trajectory estimation accuracy. Our method also identifies scenarios where certain estimation accuracy is unachievable with the given system and sensor characteristics.
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