Visual–inertial estimation of velocity for multicopters based on vision motion constraint

Global optical flow-based estimation of velocity for multicopters using monocular vision in GPS-denied environments

The NewSpace landscape for microlaunchers is highly competitive. The cost is one of the main drivers in this competition. Several microlauncher companies have decided to outsource the GNC sub-system to external providers, procuring low-cost commercial-of-the-shelf solutions. Lately, Astos has been involved in several activities related with GNC for (micro-)launchers. Astos’ work has been focused on providing GNC development tools and flexible and easily reconfigurable GNC software that can be applied to a wide range of launch vehicles. Navigation is one of the main drivers for launcher orbital injection accuracy and it is also one of the main drivers for cost in the GNC sub-system. Usually, this cost is driven by the Inertial Navigation System (INS) or Inertial Measurement Unit (IMU), in which expensive technologies are employed such as ring-laser gyroscopes. In traditional inertial propagation algorithms, errors are accumulated due to integration resulting in a steady divergence of the position and attitude estimate. Therefore, the performance of the navigation mainly depends on the performance of the IMU. This makes an investment into a highly accurate inertial sensors, especially the gyroscope, necessary to meet the navigation requirements. Lately, the use of hybrid navigation solutions that fuse the inertial measurements with Global Navigation Satellite Systems (GNSS) measurements has diminished, in some degree, the reliance of the navigation algorithm on the IMU performance. Although this is true for translational states, as position and velocity, the same cannot be said with the same degree of validity for attitude determination and, more specifically, for roll angle estimation. One possible way to reduce the cost of inertial sensors is by using microelectromechanical systems (MEMS). The advantages of using this technology, apart from the large reduction in cost, are the significant saves in mass and power required to operate these sensors. The disadvantage is clearly the lower performance, especially in high-vibration environments such as encountered in launcher vehicles. Lately, efforts to improve the performance of MEMS navigation solutions were driven by the demand of industries such as the wearables and unmanned aerial systems. This led to the development of solutions that utilize multiple low-cost inertial measurement units. By that, the hardware cost is not gravely increased in exchange for higher navigation accuracy, reliability, and redundancy. In this paper, a navigation algorithm is proposed that incorporates the measurements from multiple low-grade MEMS IMUs. The algorithm implements parallel Extended Kalman Filters (EKF), one for each IMU, combining the inertial measurements with the position, velocity, and time solutions provided by a single GNSS receiver. The algorithm has a loosely coupled architecture and the inertial propagation is regularly reset using the fused estimates of the vehicle’s state and IMU biases (closed-loop implementation). The navigation solution, providing estimates of position, velocity, and attitude, is then a combination of the individual EKF results through weighted means based on the covariance of the individual estimates. This formulation utilizing parallel filters is called in the literature as Federated Kalman Filter architecture. An analysis will be presented relating the performance of the navigation architecture with the number of sensors used and their distribution on the launch vehicle’s body. The assessment is performed with relation to a baseline navigation architecture also implementing a loosely coupled, closed-loop, hybrid structure but employing a single low-grade IMU. The analysis will determine the most promising sensor constellation, based on navigation performance, cost and implementation complexity. The selected multiple IMU solution is then compared with two different navigation architectures: Strapdown inertial propagation utilizing a single high-grade IMU; and hybrid navigation scheme implementing a loosely coupled, closed-loop architecture with a medium grade IMU. The performance evaluation is conducted utilizing simulated measurements retrieved from high-fidelity 6 degrees-of-freedom non-linear simulator of a representative microlauncher scenario. The simulators’ dynamics, kinematics, environment, and sensor modelling are provided by the ASTOS tool. Astos intends to further study the feasibility and performance of the proposed Inertial/GNSS hybrid navigation architecture with multiple IMUs. In the roadmap for raising the algorithm’s technology readiness level, more advanced simulations are foreseen, with improved sensor error modelling and previously unmodelled dynamical effects such as flexibility and sloshing. Additionally, processor- and hardware-in-the-loop tests with real sensor data are planned.

Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.

This paper proposes a new method for estimating the vehicle sideslip angle based on a nonlinear parameter-varying observer (NPVO) using low-cost (LC) sensor measurements like LC- inertial measurements units (IMU), LC-GPS with precise point positioning (PPP) algorithm and also some on-board vehicle sensors. The proposed method is based on the nonlinear bicycle model and on a nonlinear tire model. The variation of the longitudinal velocity and acceleration in the model are considered in this work. A nonlinear parameter-varying observer is applied for estimating the model states taking into account the variability of some parameters. The performance of the proposed approach is illustrated through real data acquired with an experimental vehicle.

Nowadays object tracking is a challenge in the fields of computer vision, many algorithms are put forward to overcome the issues such as noisy, colored background, and occlusion of object and also illusion which is low and sudden changes in real-time videos, trackers cannot perform well in real-time environment. An efficient algorithm is needed for effective tracing of an object because of variation in position under various conditions. Here, the MS algorithm is used due to its efficiency and performance. MS tracking algorithm is used to obtain the position of an object by using a Kalman filter, therefore, the performance of tracking an object for different videos is evaluated but it does not improve the target localization. The advanced version is developed for tracking and representing the object in different video sequences are MS and CBWH MS using Kalman filter. Therefore, the proposed method is better than the traditional MS track, which results in the constant tracking of an object throughout the video. It does not get influenced by occlusions and less subjected to background cluttering.

In this paper, a Mean Shift algorithm based on Least Square prediction is proposed. Based on the continuity of target's trace, the Least Square Mean Shift (LSMS) algorithm uses the result of Least Square prediction as the initial search center of Mean Shift algorithm. Then, Mean Shift is applied to get the final target position. The computational complexity is reduced by limiting the number of iterations of Mean shift. Experimental results show that, compared with traditional Mean Shift algorithm, the proposed algorithm improves the real-time realization. Meanwhile, it has better performance on fast moving targets and non-linear moving targets.

On-board implementation and experimental validation of collaborative transportation of loads with multiple UAVs

A distributed position and orientation system (DPOS) can provide abundant motion parameters for multi-task remote sensing loads to conduct its motion compensation. The motion parameters of imaging loads equipped with slave Inertial Measurement Units (IMUs) can be obtained by transfer alignment from master system to slave IMU. However, considering the volume, weight, and cost of the slave IMU, it is impossible to install IMUs near all the remote sensing loads in engineering practice. Aiming at the problem, a global estimation method based on spatial-temporal Kalman filter (STKF) for DPOS is proposed to estimate the motion parameters of remote sensing loads not equipped with slave IMUs. Combining kriging and Kalman filter, and taking into account the spatial-temporal correlation between points, spatial-temporal Kalman filter can make full use of the useful information of the data and enable us to obtain the optimal estimation in time and space. In order to evaluate the effectiveness of the proposed method, the semi-physical simulation based on flight experiment is conducted. The results show that the proposed method not only can realize the global estimation, but also provides us some new insights into the layout scheme of DPOS.

A sufficient condition for the convergence of the mean shift algorithm with Gaussian kernel

The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.

Robust estimation of walking robots velocity and tilt using proprioceptive sensors data fusion

Maintaining the navigation performance with respect to a certain standard is of prime importance in civil aviation operations. In global navigation satellite system (GNSS) based position estimates, the height component is less accurate specifically due to the receiver clock bias. Further, the height component is of high relevance in all phases of flight navigation. With the concept of receiver clock modeling (RCM), sometimes called as clock coasting, the accuracy of the height component could be improved by a large extent. In this paper, we present experimental results of code-based flight navigation computed using two different methods. GNSS observations are recorded on an aerial flight for about three hours with multiple GNSS receivers and an inertial measurement unit (IMU), some of these receivers are connected with external atomic clocks. Data captured is processed post-flight; position and clock bias are estimated at first using multi-GNSS code observations with a Linearized Kalman filter (LKF) without applying the concept of RCM; later using LKF approach and applying the concept of RCM. Finally, the estimated positions are compared with the reference trajectory and the topocentric coordinate differences are evaluated using both methods. Experimental results demonstrate that the precision in the height component is improved by about 80% using GPS and GLONASS observations with RCM applied compared to a positioning solution without applying RCM. There is no significant difference in the horizontal coordinates for the navigation solutions computed using the two different methods.

When the wearer of Wearable Human-Machine Interaction System (WHMIS) is talking a strenuous walk, the Inertial Measurement Units (IMUs) installed on the extremities are likely to be beyond the range of design specification of the IMU sensors. This overrange leads to poor navigation performances of the system. To overcome the problem, this paper proposes a deep learning-based method to realize robust autonomous navigation. Specifically, the Faster Region-Convolutional Neural Network (R-CNN) is built to recognize the gait types of the wearer. For each gait type, the inertial data of the wearer's thigh and the foot are collected at the same frequency as the training samples, and then used to construct a correspondent Gated Recurrent Unit-Support Vector Regression (GRU-SVR) deep hybrid neural network model for virtual IMU simulation. Then, the virtual foot-mounted IMU will be constructed in real-time by the physical thigh-mounted IMU based on the neural network models mentioned above. Once the over-range of the physical foot-mounted IMU is detected, the virtual foot inertial information will be used to reconstruct the WHMIS navigation system. Experimental results show that the proposed navigation method has the ability of fault tolerance in complex gaits, and the 3D positioning performance of the reconstructed navigation system is approximately equivalent to that of the fault-free WHMIS navigation system.

AlliedSignal Defense & Space Systems is ‘developing a miniaturized low-cost Inertial Measurement Unit (IMU) with tactical performance for missiles and munitions and for commercial applications. Many technology solutions are available that satisfy performance. This IMU design architecture ias established’ addressing cost as the primary performance parameter and then selecting the best technical performance solution to meet the cost goals. The, salient feature of the IMU is the use of two new inertial sensors designed primarily to significantly reduce cost through the use of micro-machined optoelectronic systems (MOES) for the gyro and’ micromachined electromechanical systems (MEMS) accelerometer technology. The SiMMA, Silicon Micro-Machined Accelerometer, provides the first product launch of tactical and inertial grade precision accelerometers. The SiFORS, Silicon Fiber-Optic Rotation Sensor, is an all silicon integrated optics gyro with fiber-optic sensing coil. ~The focus of theis, paper will be on the SiFORS gyro. In this paper we will be describe the design architecture and error budgets and present validating performance.’ 1. SiFORSTG IMU Introduction ’ AlliedSignal has developed a highly integrated very low cost Inertial Measurement Unit (IMU) based on Silicon integrated optics Fiber Optics Rotation Sensor’ (SiFORS) using MOES, Micro-machined OpticalElectronic Systems, and MEMS accelerometer technologies. The SiFORS IMU is environmentally robust and is currently, used in gun-hardened applications. The SiFORS lMU provides 600 Hz ,body rate and acceleration autopilot information and generates accurate. 100 Hi ‘measurements of angle (A@) and velocity (AV) for navigation applications. Its programmable data format and scaling capabilities allow a wide range of inertial navigation applications with minimum ‘design effort. Extensive performance 1 American Institute of Aeronautics and Astronautics 3 evaluations have been performed and the results are in close agreement with the predicted performance envelope. 1.1. Accelerometers in SiFORS IMU L Many solutions are available that satisfy the role of the accelerometer for all IMU applications and the. modular architecture of the’SiFORS lMU provides the flexibility for accelerometer selection. AlliedSignal’s RRA500 is a good accelerometer choice for the l-5 mg bias stability ‘range and the AlliedSignal SiMMATM, Silicon Micro-Machined Accelerometer, a. developing MEMS device, will soon be available in this performance range to further lower the IMU price. The gun-hardened system uses the Fndevco 7591 that provides, 10 mg performance after 12,000g launch shock. 1.2. Focus on SiFORS Rotation Sensor In this paper, we focus on the development and performance of the SiFORS, Silicon Fiber Optic Rotation Sensor.’ This sensor has a unique design employing the best features of both fiber optics and MOES processing. A key component of the SiFORS rotation sensor is the silicon integrated optics MultiFunction Chip (MFC), which provides the optical ( bench and low noise electronic detection capabilities for this Fiber Optic Gyroscope (FOG). An important feature of FOG technology is its performance scalability. The well understood FOG theory has facilitated performance predictions during ,.’ our SiFORS design and development phase. It also provides a low risk approach to developing performance’grade levels for various applications with the benefit being the lowest cost IMU for each performance grade. Since the entire MFC is fabricated using silicon technology, it can be produced with very high yield and is therefore ‘ideally suited for high volume, .low ‘. Copyright

A mean shift algorithm has gained special attention in recent years due to its simplicity to enable real-time tracking. However, the traditional mean shift tracking algorithm can fail to track target under occlusions. In this paper we propose a novel technique which alleviates the limitation of mean shift tracking. Our algorithm employs the Kalman filter to estimate the target dynamics information. Moreover, the proposed algorithm performs the background check process to calculate the similarity which expresses how similar to target the background is. We then find the exact target position combining the motion estimation by Kalman filter and the color based estimation by the mean shift algorithm based on the similarity value. Therefore, the proposed algorithm can robustly track targets under several types of occlusion, while the mean shift and mean shift-Kalman filter algorithms fail.