scholarly journals ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7840
Author(s):  
Fabien Colonnier ◽  
Luca Della Vedova ◽  
Garrick Orchard
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Pose Estimation ◽  
Computational Cost ◽  
Great Promise ◽  
Low Latency ◽  
Worst Case ◽  
Mapping Algorithm ◽  
Multiple Sensors ◽  
Event Based

Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution.

scholarly journals Application of the extended Kalman filter to Lidar pose estimation

2021 ◽  
Author(s):  
Marcin Kuryllo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Pose Estimation

Application of the extended Kalman filter to Lidar pose estimation

Extended Kalman Filter Based Quadrotor State Estimation Based on Asynchronous Multisensor Data

2015 ◽  
Author(s):  
Mohammad Sarim ◽  
Alireza Nemati ◽  
Manish Kumar ◽  
Kelly Cohen
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Unmanned Aerial Vehicles ◽  
Extended Kalman Filter ◽  
Real World ◽  
Flight Test ◽  
Communication Delays ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Multisensor Data

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

scholarly journals Application of the extended Kalman filter to Lidar pose estimation

2021 ◽  
Author(s):  
Marcin Kuryllo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Pose Estimation

Application of the extended Kalman filter to Lidar pose estimation

scholarly journals Pose estimation by extended Kalman filter using noise covariance matrices based on sensor output

2020 ◽  
Author(s):  
AYUKO SAITO ◽  
Satoru Kizawa ◽  
Yoshikazu Kobayashi ◽  
Kazuto Miyawaki
Keyword(s):  
Kalman Filter ◽  
Motion Analysis ◽  
Extended Kalman Filter ◽  
Pose Estimation ◽  
Lower Limb ◽  
Covariance Matrices ◽  
Motion Sensors ◽  
Sensor Output ◽  
Joint Angles ◽  
Noise Covariance

Abstract This paper presents an extended Kalman filter for pose estimation using noise covariance matrices based on sensor output. Compact and lightweight nine-axis motion sensors are used for motion analysis in widely various fields such as medical welfare and sports. A nine-axis motion sensor includes a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. Information obtained from the three sensors is useful for estimating joint angles using the Kalman filter. The extended Kalman filter is used widely for state estimation because it can estimate the status with a small computational load. However, determining the process and observation noise covariance matrices in the extended Kalman filter is complicated. The noise covariance matrices in the extended Kalman filter were found for this study based on the sensor output. Postural change appears in the gyroscope output because the rotational motion of the joints produces human movement. Therefore, the process noise covariance matrix was determined based on the gyroscope output. An observation noise covariance matrix was determined based on the accelerometer and magnetometer output because the two sensors’ outputs were used as observation values. During a laboratory experiment, the lower limb joint angles of three participants were measured using an optical 3D motion analysis system and nine-axis motion sensors while participants were walking. The lower limb joint angles estimated using the extended Kalman filter with noise covariance matrices based on sensor output were generally consistent with results obtained from the optical 3D motion analysis system. Furthermore, the lower limb joint angles were measured using nine-axis motion sensors while participants were running in place for about 100 seconds. The experiment results demonstrated the effectiveness of the proposed method for human pose estimation.

scholarly journals On-board spacecraft relative pose estimation with high-order extended Kalman filter

2019 ◽  
Vol 158 ◽  
pp. 55-67 ◽  
Author(s):  
Francesco Cavenago ◽  
Pierluigi Di Lizia ◽  
Mauro Massari ◽  
Alexander Wittig
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Pose Estimation ◽  
High Order ◽  
Relative Pose

Extended Kalman Filter for Spacecraft Pose Estimation Using Dual Quaternions

2015 ◽  
Vol 38 (9) ◽  
pp. 1625-1641 ◽  
Author(s):  
Nuno Filipe ◽  
Michail Kontitsis ◽  
Panagiotis Tsiotras
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Pose Estimation ◽  
Dual Quaternions

scholarly journals Direct Torque Control of Sensorless Induction Machine Drives: A Two-Stage Kalman Filter Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Jinliang Zhang ◽  
Longyun Kang ◽  
Lingyu Chen ◽  
Boyu Yi ◽  
Zhihui Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Cost ◽  
Direct Torque Control ◽  
Induction Machine ◽  
Torque Control ◽  
Two Stage ◽  
Torque Estimation ◽  
Filter Approach ◽  
Stator Flux

Extended Kalman filter (EKF) has been widely applied for sensorless direct torque control (DTC) in induction machines (IMs). One key problem associated with EKF is that the estimator suffers from computational burden and numerical problems resulting from high order mathematical models. To reduce the computational cost, a two-stage extended Kalman filter (TEKF) based solution is presented for closed-loop stator flux, speed, and torque estimation of IM to achieve sensorless DTC-SVM operations in this paper. The novel observer can be similarly derived as the optimal two-stage Kalman filter (TKF) which has been proposed by several researchers. Compared to a straightforward implementation of a conventional EKF, the TEKF estimator can reduce the number of arithmetic operations. Simulation and experimental results verify the performance of the proposed TEKF estimator for DTC of IMs.

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