Relative Position Estimation Research Articles

Multi-sensor-based detection and tracking of moving objects for relative position estimation in autonomous driving conditions

Moving object detection (MOD) technology was combined to include detection, tracking and classification which provides information such as the local and global position estimation and velocity from around objects in real time at least 15 fps. To operate an autonomous driving vehicle on real roads, a multi-sensor-based object detection and classification module should carry out simultaneously processing in the autonomous system for safe driving. Additionally, the object detection results must have high-speed processing performance in a limited HW platform for autonomous vehicles. To solve this problem, we used the Redmon in DARKNET-based ( https://pjreddie.com/darknet/yolo ) deep learning method to modify a detector that obtains the local position estimation in real time. The aim of this study was to get the local position information of a moving object by fusing the information from multi-cameras and one RADAR. Thus, we made a fusion server to synchronize and converse the information of multi-objects from multi-sensors on our autonomous vehicle. In this paper, we introduce a method to solve the local position estimation that recognizes the around view which includes the long-, middle- and short-range view. We also describe a method to solve the problem caused by a steep slope and a curving road condition while driving. Additionally, we introduce the results of our proposed MOD-based detection and tracking estimation to achieve a license for autonomous driving in KOREA.

Linear Integration of Tactile and Non-tactile Inputs Mediates Estimation of Fingertip Relative Position.

While skin, joints and muscles receptors alone provide lower level information about individual variables (e.g., exerted limb force and limb displacement), the distance between limb endpoints (i.e., relative position) has to be extracted from high level integration of somatosensory and motor signals. In particular, estimation of fingertip relative position likely involves more complex sensorimotor transformations than those underlying hand or arm position sense: the brain has to estimate where each fingertip is relative to the hand and where fingertips are relative to each other. It has been demonstrated that during grasping, feedback of digit position drives rapid adjustments of fingers force control. However, it has been shown that estimation of fingertips' relative position can be biased by digit forces. These findings raise the question of how the brain combines concurrent tactile (i.e., cutaneous mechanoreceptors afferents induced by skin pressure and stretch) and non-tactile (i.e., both descending motor command and joint/muscle receptors signals associated to muscle contraction) digit force-related inputs for fingertip distance estimation. Here we addressed this question by quantifying the contribution of tactile and non-tactile force-related inputs for the estimation of fingertip relative position. We asked subjects to match fingertip vertical distance relying only on either tactile or non-tactile inputs from the thumb and index fingertip, and compared their performance with the condition where both types of inputs were combined. We found that (a) the bias in the estimation of fingertip distance persisted when tactile inputs and non-tactile force-related signals were presented in isolation; (b) tactile signals contributed the most to the estimation of fingertip distance; (c) linear summation of the matching errors relying only on either tactile or non-tactile inputs was comparable to the matching error when both inputs were simultaneously available. These findings reveal a greater role of tactile signals for sensing fingertip distance and suggest a linear integration mechanism with non-tactile inputs for the estimation of fingertip relative position.

Adaptive Cooperative Localization Using Relative Position Estimation for Networked Systems With Minimum Number of Communication Links

In this paper, a novel solution for cooperative localization problem involving a network with multiple mobile agents accessing to only one beacon agent is presented. The solution can be applied to a network with a minimum number of communication links (subjecting the network topology to be an undirected spanning tree). The objective is to provide each agent in the network with information on its absolute position without using any on-board global positioning sensors such as GPS receivers. The proposed solution uses an adaptive relative position estimation algorithm for each pair of neighboring mobile agents. The proposed estimation algorithm requires each agent to measure the relative inter-agent distance. The local velocity vector is also measured and transmitted to the neighboring agents. The estimation mechanism incorporating a signum function outperforms the recently established relative position estimation algorithms, in terms of positioning and tracking errors. An adaptive cooperative localization (ACL) algorithm is formed by augmenting the relative position estimation in a cooperative observer scheme suitably applicable for accomplishing localization task involving a network of mobile agents. The salient feature of the proposed ACL algorithm is that the communication graph among the agents needs only to have one undirected path between two agents in the network. Such convenience promotes easy practical implementation and lite computation for each agent. The proof of the proposed algorithm is provided using the Lyapunov stability theorem. Three simulation case studies are presented to evaluate the performance of the solution in different scenarios, including the stationary and moving beacon agent as well as the non-cooperatively controlled and cooperatively controlled network of mobile agents. The comparative studies reveal that the ACL algorithm is superior to the recently investigated linear-convex algorithm. The number of communication links required for the localization task to be carried out by the proposed algorithm is minimum, thus promoting a more preferable energy-efficient solution.

Integrated Relative Localization and Leader–Follower Formation Control

This paper concentrates on the integration of relative localization and the formation control over leader–follower networks. First, we develop a consensus-like relative localization scheme for each agent to estimate the real-time relative positions of its neighbors by merely using velocity as well as distance-related measurements and local communications under the assumption that the local frames of all agents share a common orientation. It is shown that the relative position estimate exponentially converges to its true value under a persistent excitation condition on relative velocity between the two agents. Second, an integrated relative localization and a leader–follower formation control are developed by combining the proposed relative localization scheme and a complex Laplacian-based formation control scheme. It is proven that the integrated system globally asymptotically converges to a stationary similar formation in the case of error-free initial relative position estimates while globally uniformly bounds in a neighborhood of the prescribed formation in the case with initial relative position estimation errors. Furthermore, based on the two degrees of freedom of similar formation on translation, the integrated scheme is also generalized to achieve moving similar formation control. Finally, simulations are presented to illustrate the effectiveness of our theoretical results.

Uncooperative Spacecraft Relative Navigation With LIDAR-Based Unscented Kalman Filter

Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters.

Relative Position Estimation of Detected Robots for Formation Control

Relative Position Estimation of Detected Robots for Formation Control

Characteristics of Relative Navigation Algorithms Using Laser Measurements and Laser-GPS Combined Measurements

This paper presents a satellite relative navigation strategy for formation flying, which chooses an appropriate navigation algorithm according to the operating environment. Not only global positioning system (GPS) measurements, but laser measurements can also be utilized to determine the relative positions of satellites. Laser data is used solely or together with GPS measurements. Numerical simulations were conducted to compare the relative navigation algorithm using only laser data and laser data combined with GPS data. If an accurate direction of laser pointing is estimated, the relative position of satellites can be determined using only laser measurements. If not, the combined algorithm has better performance, and is irrelevant to the precision of the relative angle data between two satellites in spherical coordinates. Within 10 km relative distance between satellites, relative navigation using double difference GPS data makes more precise relative position estimation results. If the simulation results are applied to the relative navigation strategy, the proper algorithm can be chosen, and the relative position of satellites can be estimated precisely in changing mission environments.

Experimental Study of Spacecraft Pose Estimation Algorithm Using Vision-based Sensor

This paper presents a vision-based relative pose estimation algorithm and its validation through both numerical and hardware experiments. The algorithm and the hardware system were simultaneously designed considering actual experimental conditions. Two estimation techniques were utilized to estimate relative pose; one was a nonlinear least square method for initial estimation, and the other was an extended Kalman Filter for subsequent on-line estimation. A measurement model of the vision sensor and equations of motion including nonlinear perturbations were utilized in the estimation process. Numerical simulations were performed and analyzed for both the autonomous docking and formation flying scenarios. A configuration of LED-based beacons was designed to avoid measurement singularity, and its structural information was implemented in the estimation algorithm. The proposed algorithm was verified again in the experimental environment by using the Autonomous Spacecraft Test Environment for Rendezvous In proXimity (ASTERIX) facility. Additionally, a laser distance meter was added to the estimation algorithm to improve the relative position estimation accuracy. Throughout this study, the performance required for autonomous docking could be presented by confirming the change in estimation accuracy with respect to the level of measurement error. In addition, hardware experiments confirmed the effectiveness of the suggested algorithm and its applicability to actual tasks in the real world.

The effects of using passing beams during the day in real traffic conditions

This paper presents a study on the implications for road safety of the use of passing beams during the day. The genesis of the problem and conditions that have led to the use of passing beams by vehicles during the day is presented first. Then, the photometric requirements for passing beams are evaluated in terms of their signal role, comparing them with the requirements for daytime running lights. The main part of this paper presents a report on field tests carried out under real road conditions. The tests were done in order to measure the impact of using passing beams by day on the distance at which the oncoming vehicle could be detected. Also, the correctness of the estimation of relative positions of oncoming vehicles at the same or different distances was examined with different combinations of passing beams on or off in the tested vehicles. The research confirms the effectiveness of using passing beams during the day and the need to harmonize the obligation to use passing beams.

Comparison of filtering techniques for relative attitude estimation of uncooperative space objects

Nowadays, one of the most active research fields in space engineering is autonomous relative navigation around uncooperative objects. A common approach used to tackle this problem is through vision-based pose determination techniques. This paper investigates the possibility of using non-linear filtering techniques to improve the attitude estimation performance of vision-based methods. Furthermore, a simulation study is presented to compare the proposed nonlinear techniques with the multiplicative extended Kalman filter for attitude estimation. First-order and second-order nonlinear filters are adapted, implemented and tested for relative attitude estimation. Finally, the consequences of uncertainty in the knowledge of the target inertia matrix are investigated.

Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments

Recently, monocular localization has attracted increased attention due to its application to indoor navigation and augmented reality. In this paper, a drift-aware monocular localization system that performs global and local localization is presented based on a pre-constructed dense three-dimensional (3D) map. In global localization, a pixel-distance weighted least squares algorithm is investigated for calculating the absolute scale for the epipolar constraint. To reduce the accumulative errors that are caused by the relative position estimation, a map interaction-based drift detection method is introduced in local localization, and the drift distance is computed by the proposed line model-based maximum likelihood estimation sample consensus (MLESAC) algorithm. The line model contains a fitted line segment and some visual feature points, which are used to seek inliers of the estimated feature points for drift detection. Taking advantage of the drift detection method, the monocular localization system switches between the global and local localization modes, which effectively keeps the position errors within an expected range. The performance of the proposed monocular localization system is evaluated on typical indoor scenes, and experimental results show that compared with the existing localization methods, the accuracy improvement rates of the absolute position estimation and the relative position estimation are at least 30.09% and 65.59%, respectively.

Real Time Precise Relative Positioning with Moving Multiple Reference Receivers.

The stationary reference receiver with precisely known coordinates is difficult to establish in some special real-time relative positioning applications. To improve the relative position estimation accuracy and the reliability simultaneously for the RTK without a precisely known reference receiver, multiple receivers mounted on a moving platform are used as the base station. A code and phase measurement fusion model is proposed to reduce the communication burden and generate measurements at any virtual position where it is inconvenient to install the GPS receiver. To keep the integer property of the ambiguity of fused phase measurements, the RTK method with the moving reference receivers is proposed by implementing the integer ambiguity transformation and error absorption strategy based on the known geometry of multiple receivers. Static and kinematic experiments were carried out to evaluate the performance of the proposed relative positioning method. When compared with the single-receiver solution, static results have shown that the proposed method can improve position accuracy by 15.9% and 15.7% for the horizontal and the vertical component, respectively. The kinematic results have shown that the proposed method can achieve position accuracy enhancement by 26.9% for the vertical component.

Ranging-aided relative navigation of multi-platforms

This paper proposes a relative attitude and distance estimation algorithm based on pairwise range measurements between vehicles as well as inertial measurement of each platform. The solution of Wahba’s Problem is introduced to compute the relative attitude between multi-platforms with the sampled pairwise ranges, in which the relative distance estimation is derived and the estimation error distributions are analyzed. An extended Kalman filter is designed to fuse the estimated attitude and distance with the inertial measurement of each platform. The relative poses between platforms are determined without any external aided measurement. To show this novelty, a real testbed is constructed by our research lab. And the experiment results are positive.

Shipboard Landing Control Enabled by an Uncertainty and Disturbance Estimator

This paper presents an autonomous landing control approach for a quadrotor unmanned aerial vehicle subject to wind disturbance and three-dimensional movements of the landing platform. To achieve an accurate relative position estimation of the quadrotor to the landing platform, a camera, a distance sensor, and a single-board computer are integrated to the quadrotor. The coordinate transformation is introduced to deal with the constraint that only the relative position information is available. The impacts of unknown ship motions are treated as part of the lumped uncertainty terms. Then, the uncertainty and disturbance estimator-based controllers are developed to achieve the accurate relative position control of the quadrotor while dealing with unknown ship motions, ground effect, state couplings, and external disturbances. The uncertainty and disturbance estimator filter is designed based on the internal model principle to enhance the performance of the developed controller. The reference model of the relative altitude controller is modified to ensure the accurate tracking of descending commands. To maximize the capability of the developed controller, a parameter selection guideline based on the derived ship heave acceleration spectrum is provided. Both numerical simulations and flight experiments are carried out to demonstrate the effectiveness of the developed approach.

Design of parallel adaptive extended Kalman filter for online estimation of noise covariance

Purpose The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics. Design/methodology/approach Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value. Findings The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF. Practical implications The presented algorithm is applicable for spacecraft relative attitude and position estimation. Originality/value The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

Localization using relative position estimation by image comparison

Localization using relative position estimation by image comparison

Adaptive Iterated Extended KALMAN Filter for Relative Spacecraft Attitude and Position Estimation

AbstractThis paper presents a novel adaptive iterated extended Kalman filter (AIEKF) for relative position and attitude estimation, taking into account the influence of model uncertainty. Considering a nonlinear stochastic discrete‐time system with unknown disturbance, the AIEKF algorithm adopts the Gauss‐Newton iterative optimization steps to implement a maximum a posteriori (MAP) estimation, and the switch‐mode combination technique is used to achieve the adaptive capability. The mean‐square estimation error (MSE) of the state estimate is derived. It is proved that the AIEKF can yield a smaller MSE than that of the traditional extended Kalman filter (EKF) or iterated extended Kalman filter (IEKF). The performance advantage of the AIEKF is illustrated via Monte Carlo simulations on a typical relative position and attitude estimation application. Through comparisons in different scenarios, the presented algorithm is shown to improve adaptability and ensure estimation accuracy.

Role of digit placement control in sensorimotor transformations for dexterous manipulation.

Dexterous manipulation relies on the ability to modulate grasp forces to variable digit position. However, the sensorimotor mechanisms underlying such critical ability are not well understood. The present study addressed whether digit force-to-position modulation relies entirely on feedback of digit placement and force, or on the integration of such feedback with motor commands responsible for digit positioning. In two experiments, we asked 25 subjects to estimate the index fingertip position relative to the thumb (perception test) or to grasp and lift an object with an asymmetrical mass distribution while preventing object roll (action test). Both tests were performed after subjects' digits were placed actively or passively at different distances (active and passive condition, respectively) and without visual feedback. Because motor commands for digit positioning would be integrated with position and force feedback in the active condition, we hypothesized this condition to be characterized by greater accuracy of digit position estimation and digit force-to-position modulation. Surprisingly, discrimination of digit position and force-to-position modulation was statistically indistinguishable in the active and passive conditions. We conclude that voluntary commands for digit positioning are not essential for accurate estimation of finger position or modulation of digit forces to variable digit position. Thus digit force-to-position modulation can be implemented by integrating sensory feedback of digit position and voluntary commands of digit force production following contact.NEW & NOTEWORTHY This study was designed to understand the sensorimotor mechanisms underlying digit force-to-position modulation required for manipulation. Surprisingly, estimation of relative digit position and force-to-position modulation was accurate regardless of whether the digits were passively or actively positioned. Therefore, accurate estimation of digit position does not require an efference copy of active digit positioning, and the hypothesized advantage of active over passive movement on estimation of end-point position appears to be task and effector dependent.

Space Robot Relative Navigation for Debris Removal

The structure of a space target, and estimates of its relative pose (position and orientation) and motion are the primary first tasks of any formation flight, including missions to remove space debris. Acquiring these estimates for high-speed tumbling objects is very challenging, due to the lack of prior information about the target’s structure and motion. This paper proposes a method to estimate the relative position, linear and angular velocities, and attitude of space debris, based on vision measurements. The suggested approach employs a stereo camera to track a set of feature points on the target spacecraft, and an Unscented Kalman Filter (UKF) to perform the estimation procedure. The projection of tracked feature points on the two cameras creates an observation model of the filters and the structure of a non-cooperative spacecraft was determined by estimating the feature points’ position. The relative attitude estimation is derived through the Modified Rodrigues Parameters (MRPs). A number of numerical simulations were conducted to validate the accuracy and stability of this solution, and the results indicated acceptable convergence of estimation errors for pose and motion estimation.

Vision algorithms for fixed-wing unmanned aerial vehicle landing system

Autonomous landing has become a core technology of unmanned aerial vehicle (UAV) guidance, navigation and control system in recent years. As a novel autonomous landing approach, computer vision has been studied and applied in rotary-wing UAV landing successfully. This paper aims to fixed-wing UAV and focus on two problems: how to find runway only depending on airborne front-looking camera and how to align UAV with the designated landing runway. The paper can be divided into two parts to solve above two problems respectively. In the first part, the paper firstly presents an algorithm of region of interest (ROI) detection, which is based on spectral residual saliency map, and then an algorithm of feature vector extraction based on sparse coding and spatial pyramid matching (SPM) is proposed, finally, ROI including designated landing runway is recognized by a linear support vector machine. In the second part, the paper presents an approach of relative position and pose estimation between UAV and landing runway. Estimation algorithm firstly selects five feature points on the runway surface, and then establishes a new earth-fixed reference frame, finally uses orthogonal iteration to estimate landing parameters including three parameters of distance, height and offset, and three pose parameters of roll, yaw, pitch. The experimental results verify the effectiveness of the algorithms proposed in this paper.