Bridging GNSS Outages with IMU and Odometry: A Case Study for Agricultural Vehicles

Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle’s state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy.

Error-state Kalman filter for lower-limb kinematic estimation: Evaluation on a 3-body model

Human lower-limb kinematic measurements are critical for many applications including gait analysis, enhancing athletic performance, reducing or monitoring injury risk, augmenting warfighter performance, and monitoring elderly fall risk, among others. We present a new method to estimate lower-limb kinematics using an error-state Kalman filter that utilizes an array of body-worn inertial measurement units (IMUs) and four kinematic constraints. We evaluate the method on a simplified 3-body model of the lower limbs (pelvis and two legs) during walking using data from simulation and experiment. Evaluation on this 3-body model permits direct evaluation of the ErKF method without several confounding error sources from human subjects (e.g., soft tissue artefacts and determination of anatomical frames). RMS differences for the three estimated hip joint angles all remain below 0.2 degrees compared to simulation and 1.4 degrees compared to experimental optical motion capture (MOCAP). RMS differences for stride length and step width remain within 1% and 4%, respectively compared to simulation and 7% and 5%, respectively compared to experiment (MOCAP). The results are particularly important because they foretell future success in advancing this approach to more complex models for human movement. In particular, our future work aims to extend this approach to a 7-body model of the human lower limbs composed of the pelvis, thighs, shanks, and feet.

A hybrid CAI/IMU solution for higher navigation performance

<p>Atom Interferometers as inertial sensors were getting quite some interest in the last decade. Several attempts have been made to combine the two sensors (i.e. classical inertial measurement units IMU and cold atom interferometers), mainly with the goal to use the atom interferometer as main sensor, and support it with different conventional sensors in order to suppress noise and achieve maximum sensitivity and long-term stability.<br>We present a quite promising combination of both sensors in an error state extended Kalman Filter framework aimed especially on further improving the performance of a conventional high end IMU. While the full potential of the cold atom interferometer is not yet entirely exploited in this combination, first simulations in terrestrial applications with small and even larger change of inertial forces show an increase of the navigation solution precision by a factor of 20 and more.</p>

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances.

Error-State Extended Kalman Filter-Based Sensor Fusion for Optimized Drive Train Regulation of an Autonomous PHEV

An efficient state estimator is critical for the development of an autonomous plug-in hybrid electric vehicle (PHEV). To achieve effective autonomous regulation of the powertrain, the latency period and estimation error should be minimum. In this work, a novel error state extended kalman filter (ES-EKF)-based state estimator is developed to perform sensor fusion of data from light detection and ranging sensor (LIDAR), the inertial measurement unit sensor (IMU), and the global positioning system (GPS) sensors, and the estimation error is minimized to reduce latency. The estimator will provide information to an intelligent energy management system (IEMS) to regulate the powertrain for effective load sharing in the PHEV. The integration of the sensor fusion data with the vehicle model is simulated in MATLAB environment. The PHEV model is fed with the proposed state estimator output, and the response parameters of the PHEV are monitored.

Portable Gait Lab: Estimating Over-Ground 3D Ground Reaction Forces Using Only a Pelvis IMU

As an alternative to force plates, an inertial measurement unit (IMU) at the pelvis can offer an ambulatory method for measuring total center of mass (CoM) accelerations and, thereby, the ground reaction forces (GRF) during gait. The challenge here is to estimate the 3D components of the GRF. We employ a calibration procedure and an error state extended Kalman filter based on an earlier work to estimate the instantaneous 3D GRF for different over-ground walking patterns. The GRF were then expressed in a body-centric reference frame, to enable an ambulatory setup not related to a fixed global frame. The results were validated with ForceShoesTM, and the average error in estimating instantaneous shear GRF was 5.2 ± 0.5% of body weight across different variable over-ground walking tasks. The study shows that a single pelvis IMU can measure 3D GRF in a minimal and ambulatory manner during over-ground gait.

Correction: Liu, W. et al. Error Overboundings of KF-Based IMU/GNSS Integrated System Against IMU Faults. Sensors 2019, 19, 4912

To clearly highlight the differences between the error overboundings of error-state EKFs and full-state EKFs [...]