本文提出了一种基于扩展卡尔曼滤波的鲁棒位姿估计算法，该算法利用加速度计、速率陀螺仪、单目视觉和超宽带雷达的测量数据。该传感器融合与位姿估计算法融合了基于马氏距离的异常值剔除机制，并对测量值进行降权处理，以在雷达收发器因测距限制导致测量缺失而出现突发测距值的情况下，保证滤波器的鲁棒性能。研究人员进一步通过使用低成本雷达、IMU和相机传感器的实验分析验证了该估计器。所得到的位姿估计结果被用于执行Transforming Proximity Operations and Docking Service（变形近距操作与对接服务，TPODS）卫星模块与固定目标的近距操作和对接。
英文摘要：
A robust pose estimation algorithm based on an extended Kalman filter using measurements from accelerometers, rate gyroscopes, monocular vision and ultra-wideband radar is presented. The sensor fusion and pose estimation algorithm incorporates Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust filter performance in the case of sudden range measurements led by the absence of measurements due to range limitations of radar transceivers. The estimator is further validated through an experimental analysis using low-cost radar, IMU and camera sensors. The pose estimate is utilized to perform proximity operations and docking of Transforming Proximity Operations and Docking Service (TPODS) satellite modules with a fixed target.
Joint axis estimation for fast and slow movements using weighted gyroscope and acceleration constraints
1 4 Department of Information Technology, Uppsala University, Uppsala, Sweden；2 3 Department of Electrical Engineering；4 Department of Mecatronics, Tecnol \'o gico de Monterrey, Mexico City, Mexico
Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filte…
This work presents a contracting hierarchical observer that fuses position and orientation measurements with an IMU to generate smooth position, linear velocity, orientation, and IMU bias estimates that are guaranteed to converge to their true values. The proposed approach is composed of two contracting observers. The first is a quaternion-based orientation observer that also estimates gyroscope bias. The output of the orientation observer serves as an input for another contracting observer that estimates position, linear velocity, and accelerometer bias thus forming a hierarchy. We show that …
Accurate state estimation using low-cost MEMS (Micro Electro- Mechanical Systems) sensors present on Commercial-off-the-shelf (COTS) drones is a challenging problem. Most UAV systems use a combination of a gyroscope, an accelerometer, and a magnetometer to obtain measurements and estimate attitude. Under this paradigm of sensor fusion, the Extended Kalman Filter (EKF) is the most popular algorithm for attitude estimation in UAVs. In this work, we propose a novel estimation technique called extended H2 filter that can overcome the limitations of the EKF, specifically with respect to computation…
Delft Center for Systems and Control, Delft University of Technology（代尔夫特理工大学代尔夫特系统与控制中心）；Department of Information Technology, Uppsala University（乌普萨拉大学信息技术系）
We present a novel algorithm for online, real-time orientation estimation. Our algorithm integrates gyroscope data and corrects the resulting orientation estimate for integration drift using accelerometer and magnetometer data. This correction is computed, at each time instance, using a single gradient descent step with fixed step length. This fixed step length results in robustness against model errors, e.g. caused by large accelerations or by short-term magnetic field disturbances, which we numerically illustrate using Monte Carlo simulations. Our algorithm estimates a three-dimensional upda…
In motion tracking of connected multi-body systems Inertial Measurement Units (IMUs) are used in a wide variety of applications, since they provide a low-cost easy-to-use method for orientation estimation. However, in indoor environments or near ferromagnetic material the magnetic field is inhomogeneous which limits the accuracy of tracking algorithms using magnetometers. Methods that use only accelerometers and gyroscopes on the other hand yield no information on the absolute heading of the tracked object. For objects connected by rotational joints with range of motion constraints we propose …
Inertial measurement units are commonly used to estimate the attitude of moving objects. Numerous nonlinear filter approaches have been proposed for solving the inherent sensor fusion problem. However, when a large range of different dynamic and static rotational and translational motions is considered, the attainable accuracy is limited by the need for situation-dependent adjustment of accelerometer and gyroscope fusion weights. We investigate to what extent these limitations can be overcome by means of artificial neural networks and how much domain-specific optimization of the neural network…
Using Inertial Sensors for Position and Orientation Estimation
Delft Center for Systems and Control, Delft University of Technology, the Netherlands（荷兰代尔夫特理工大学系统与控制中心）；Xsens Technologies B.V., Enschede, the Netherlands（荷兰恩schede市Xsens技术公司）；Department of Information Technology, Uppsala University, Sweden（瑞典乌普萨拉大学信息科技系）
Symbiosis International (Deemed University)（共生国际大学）；Central Scientific Instruments Organisation（中央科学仪器组织）；Council of Scientific and Industrial Research（科学与工业研究理事会）
This paper presents a novel method for attitude estimation of an object in 3D space by incremental learning of the Long-Short Term Memory (LSTM) network. Gyroscope, accelerometer, and magnetometer are few widely used sensors in attitude estimation applications. Traditionally, multi-sensor fusion methods such as the Extended Kalman Filter and Complementary Filter are employed to fuse the measurements from these sensors. However, these methods exhibit limitations in accounting for the uncertainty, unpredictability, and dynamic nature of the motion in real-world situations. In this paper, the ine…
