Object Location Estimation Research Articles

Biases in object location estimation: The role of rotations and translation

Spatial memory studies often employ static images depicting a scene, an array of objects, or environmental features from one perspective and then following a perspective-shift-prompt memory either of the scene or objects within the scene. The current study investigated a previously reported systematic bias in spatial memory where, following a perspective shift from encoding to recall, participants indicated the location of an object farther to the direction of the shift. In Experiment 1, we aimed to replicate this bias by asking participants to encode the location of an object in a virtual room and then indicate it from memory following a perspective shift induced by camera translation and rotation. In Experiment 2, we decoupled the influence of camera translations and rotations and examined whether adding additional objects to the virtual room would reduce the bias. Overall, our results indicate that camera translations result in greater systematic bias than camera rotations. We propose that the accurate representation of camera translations requires more demanding mental computations than camera rotations, leading to greater uncertainty regarding the location of an object in memory. This uncertainty causes people to rely on an egocentric anchor, thereby giving rise to the systematic bias in the direction of camera translation.

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person’s position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

The role of memory and perspective shifts in systematic biases during object location estimation.

In the current study, we investigated whether the introduction of perspective shifts in a spatial memory task results in systematic biases in object location estimations. To do so, we asked participants to first encode the position of an object in a virtual room and then to report its position from memory or perception following a perspective shift. Overall, our results showed that participants made systematic errors in estimating object positions in the same direction as the perspective shift. Notably, this bias was present in both memory and perception conditions. We propose that the observed systematic bias was driven by difficulties in understanding the perspective shifts that led participants to use an egocentric representation of object positions as an anchor when estimating the object location following a perspective shift.Supplementary InformationThe online version contains supplementary material available at 10.3758/s13414-022-02445-y.

The role of memory and perspective shifts in systematic biases during object location estimation

The role of memory and perspective shifts in systematic biases during object location estimation

Reasoning about location of robot-operated object based on probabilistic ontologies

Background: A robot needs to acquire the location of objects when performing daily tasks. Compared to industrial robots, a service robot faces a more complex and unstructured working environment where the location of the target object is usually uncertain. For example, due to diversity in personal habits and seasons, apples may be located in refrigerators, tables, or other places in the living environment. Methods: We propose a novel method for semantic localization of the robot-operated object based on probabilistic ontologies (PR-OWL) and multi-entity Bayesian networks (MEBN). The probabilistic web ontology language is used to describe and model the highly uncertain knowledge about the storage location of objects in the human household environment. Furthermore, the target location is inferred based on the multi-entity Bayesian network. Results: The proposed method is capable to adapting to environmental changes and achieves reliable probability estimation of object location. Experiments on simulated robotic tasks verify the effectiveness of the method. Conclusions: We show that applying PR-OWL combined with MEBN to locate the target object for the robot is feasible， which can improve the cognitive and self-adaptive ability of the robot.

Single‐object localization using multiple ultrasonic sensors and constrained weighted least‐squares method

AbstractIn this paper, an active single‐object localization system with U‐shaped ultrasonic module array is presented. The proposed algorithm in this system has two stages. The first stage is time‐of‐flight (TOF) estimation of reflected ultrasonic signals. We analyze several distortion cases of reflection signals and provide solutions to improve the performance of TOF estimation. The second stage is TOF‐based object localization. A good localization algorithm can suppress the problems caused by inaccurate TOF estimation and improve the accuracy of target positioning. We propose two object‐localization algorithms to estimate the object location. One is the unconstrained least‐squares method, and the other is the constrained weighted least‐squares method based on the eigenvalues‐analyzing technique. The simulation results suggest that the performance of the proposed constrained weighted method is better than that of the unconstrained method. In addition, the median smoother and the outlier exclusion scheme are implemented in the overall system to make the object location estimation more stable. The performance of the proposed system is confirmed by the experiment results, which show that the single‐object localization has a considerable degree of accuracy and stability.

Non-line-of-sight object location estimation from scattered light using plenoptic data.

We investigate the use of plenoptic data for locating non-line-of-sight (NLOS) objects from a scattered light signature. Using Fourier analysis, the resolution limits of the depth and transversal location estimates are derived from fundamental considerations on scattering physics and measurement noise. Based on the refocusing algorithm developed in the computer vision field, we derive an alternative formulation of the projection slice theorem in a form directly connecting the light field and a full spatial frequency spectrum including both depth and transversal dimensions. Using this alternative formulation, we propose an efficient spatial frequency filtering method for location estimation that is defined on a newly introduced mixed space frequency plane and achieves the theoretically limited depth resolution. A comparison with experimental results is reported.

Recent Advances in 3D Object Detection in the Era of Deep Neural Networks: A Survey.

With the rapid development of deep learning technology and other powerful tools, 3D object detection has made great progress and become one of the fastest growing field in computer vision. Many automated applications such as robotic navigation, autonomous driving, and virtual or augmented reality system require estimation of accurate 3D object location and detection. Under this requirement, many methods have been proposed to improve the performance of 3D object localization and detection. Despite recent efforts, 3D object detection is still a very challenging task due to occlusion, viewpoint variations, scale changes, and limited information in 3D scenes. In this paper, we present a comprehensive review of recent state-of-the-art approaches in 3D object detection technology. We start with some basic concepts, then describe some of the available datasets that are designed to facilitate the performance evaluation of 3D object detection algorithms. Next, we will review the state-of-the-art technologies in this area, highlighting their contributions, importance, and limitations as a guide for future research. Finally, we provide a quantitative comparison of the results of the state-of-the-art methods on the popular public datasets.

If I were a grown-up: Children’s response to the rubber hand illusion with different hand sizes

During childhood, children’s bodies undergo rapid physical growth, which may affect their ability to accurately perceive their own bodies as well as the external environment. Concurrently, multisensory processes underlying bodily self-consciousness gradually develop. However, little is known of the relation between changes in body size and corresponding update in bodily self-consciousness. In this study, 6- to 8-year-old children experienced the “rubber hand illusion” while watching a regular (child-like) or larger (adult-like) rubber hand being touched either synchronously or asynchronously with their own real hand. We measured proprioceptive drift and subjective ownership as well as changes in weight estimation of objects. Synchronous versus asynchronous visuotactile stroking with both the regular and larger hands caused a change in subjective body ownership but did not make a difference in perceived hand location and weight estimation of objects having the same size but different weight. Intriguingly, simply viewing the regular hand led to greater proprioceptive drift and to overestimation of the objects’ weight relative to the larger hand. In addition, weight estimation was also influenced by hand size after visuotactile stroking. These results demonstrate that visuotactile information modulates the intrinsic properties of body representation in terms of size in children and further corroborate previous findings suggesting late developing optimal calibration of visual and proprioceptive signals.

LOCALIZATION ALGORITHM FOR MULTILATERATION SYSTEMS

At present, multilateration systems are becoming increasingly important in air traffic control. This is due to their significant advantages in compare with secondary surveillance radar complexes. This article solves the problem of synthesizing an algorithm for object location estimation for multilateration system operating in passive mode. The synthesized algorithm is a combination of a procedure, the result of which is a rough estimate of the observed object coordinates, and an iterative algorithm specifying the resulting solution. The rough estimate is the result of solving a linear system of equations. The iterative refinement procedure is based on the linearization of the observational equations and does not require a large number of iterations. The paper provides a comparative statistical analysis of the proposed algorithm and the known Bancroft algorithm. For an objective analysis of two algorithms, the paper derives the Cramer-Rao boundary for the correlation matrix of estimates of the observed object coordinates, which makes it possible to determine the potential accuracy of the solution of the problem. It is shown that both algorithms allow obtaining estimates, the accuracy of which is close to the potentially achievable accuracy of the object location estimate. In contrast to the Bancroft algorithm, the rough estimate of the object location is unambiguous. This virtue reduces the total amount of computations during the algorithm implementation and reduces the probability of anomalous errors.

Robust long-term correlation tracking using convolutional features and detection proposals

Abstract Correlation filter based trackers have achieved appealing performance and high efficiency in recent years. However, for long-term tracking where target objects undergo dramatic appearance variation due to heavy occlusion or out-of-view, conventional correlation filter based tracking algorithms would be distracted by irrelevant objects. Once the trained tracker loses its way, it is impossible to recover the information for the following frames as the model has drifted. In this paper, we decompose the long-term tracking task into tracking and detection. Tracker learns separate correlation filters for explicit translation and scale estimation. Specifically, in order to improve tracking accuracy, the convolutional features for translation filter are extracted, and the scale filter is learned using the target appearance sampled at different scales. Detector trains an online long-term filter and applies it to the entire frame to generate detection proposals. By exploiting these detection proposals, it helps the tracker to recover from problems such as temporary or persistent occlusions. In this way, the proposed approach could handle the model drifting problem effectively for long-term tracking with more accurate estimation of object scale and location. Extensive experimental results on large-scale benchmark sequences have shown the robustness of the proposed method.

Explorations on visual localization from active to passive

In this paper, we novelly consider visual localization in active and passive two ways, with simple definition that active localization assists device to estimate location of its interest while passive localization aids device to estimate its own location in environment. Expecting to indicate some insights into visual localization, we specifically performed two explorations on active localization and more importantly explored to upgrade them from active to passive localization with extra geometry information available. In order to produce unconstrained and accurate 2D location estimation of interested object, we constructed an active localization system by fusing detection, tracking and recognition. Based on recognition, we proposed a collaborative strategy making mutual enhancement between detection and tracking possible to obtain better performance on 2D location estimation. Meanwhile, to actively estimate semantic location of interested visual region, we employed latest state-of-the-art light weight CNN models specifically designed for efficiency and trained two of them with large place dataset in perspective of scene recognition. What’s more, using depth information available from RGB-D camera, we improved the active system for 2D location of interested object to a passive system for relative 3D location of device to the interested object. Firstly estimated was the 3D location of the interested object in the coordinate system of device, then relative location of device to the interested object in world coordinate system was deduced with appropriate assumption. Evaluations both subjectively on a RGB-D sequence obtained in a lab environment and practically on a robotic platform in an office environment indicated that the improved system was suitable for autonomous following robot. As well, the active system for rough semantic location estimation of interested visual region was promoted to a passive system for fine location estimation of device, with available 3D map describing the visited environment. In perspective of place recognition, we first adopted one of the efficient CNN models previously trained for semantic location estimation as a base to generate CNN features for both retrieval of candidate loops in the map and geometrical consistency checking of retrieved loops, then true loops were used to deduce fine location of device itself in environment. Comparison with state-of-the-art results reflected that the promoted system was adequate for long-term robotic autonomy. Achieving favorable performances, the presented four explorations have implied adequacy for elaborating on some insights into visual localization.

Comparison of Estimation Accuracy of EKF, UKF and PF Filters

Abstract Several types of nonlinear filters (EKF – extended Kalman filter, UKF – unscented Kalman filter, PF – particle filter) are widely used for location estimation and their algorithms are described in this paper. In the article filtering accuracy for non-linear form of measurement equation is presented. The results of complex simulations that compare the quality of estimation of analyzed non-linear filters for complex non-linearities of state vector are presented. The moves of maneuvering object are described in two-dimensional Cartesian coordinates and the measurements are described in the polar coordinate system. The object dynamics is characterized by acceleration described by the univariate non-stationary growth model (UNGM) function. The filtering accuracy was evaluated not only by the root-mean-square errors (RMSE) but also by statistical testing of innovations through the expected value test, the whiteness test and the WSSR (weighted sum squared residual) test as well. The comparison of filtering quality was done in the MATLAB environment. The presented results provide a basis for designing more accurate algorithms for object location estimation.

단일 카메라와 GPS를 이용한 영상 내 객체 위치 좌표 추정 기법

ADAS(Advanced Driver Assistance Systems) and street furniture information collecting car like as MMS(Mobile Mapping System), they require object location estimation method for recognizing spatial information of object in road images. But, the case of conventional methods, these methods require additional hardware module for gathering spatial information of object and have high computational complexity. In this paper, for a coordinate of road sign in single camera image, a position estimation scheme of object in road images is proposed using the relationship between the pixel and object size in real world. In this scheme, coordinate value and direction are used to get coordinate value of a road sign in images after estimating the equation related on pixel and real size of road sign. By experiments with test video set, it is confirmed that proposed method has high accuracy for mapping estimated object coordinate into commercial map. Therefore, proposed method can be used for MMS in commercial region.

A Mobile Robot Localization via Indoor Fixed Remote Surveillance Cameras.

Localization, which is a technique required by service robots to operate indoors, has been studied in various ways. Most localization techniques have the robot measure environmental information to obtain location information; however, this is a high-cost option because it uses extensive equipment and complicates robot development. If an external device is used to determine a robot’s location and transmit this information to the robot, the cost of internal equipment required for location recognition can be reduced. This will simplify robot development. Thus, this study presents an effective method to control robots by obtaining their location information using a map constructed by visual information from surveillance cameras installed indoors. With only a single image of an object, it is difficult to gauge its size due to occlusion. Therefore, we propose a localization method using several neighboring surveillance cameras. A two-dimensional map containing robot and object position information is constructed using images of the cameras. The concept of this technique is based on modeling the four edges of the projected image of the field of coverage of the camera and an image processing algorithm of the finding object’s center for enhancing the location estimation of objects of interest. We experimentally demonstrate the effectiveness of the proposed method by analyzing the resulting movement of a robot in response to the location information obtained from the two-dimensional map. The accuracy of the multi-camera setup was measured in advance.

Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment

Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future.

Performance Evaluation of Location Estimation System Using a Non Fixed Single Receiver

General location aware systems are only applied to indoor and outdoor environments using more than three transmitters to estimate a fixed object location. Those kinds of systems have environmental restrictions that require an already established infrastructure. To solve this problem, an Object Location Estimation (OLE) algorithm based on PTP (Point To Point) communication has been proposed. However, the problem with this method is that deduction of performance parameters is not enough and location estimation is very difficult because of unknown restriction conditions. From experimental tests in this research, we determined that the performance parameters for restriction conditions are a maximum transmission distance of CSS communication and an optimum moving distance interval between personal locations. In this paper, a system applied OLE algorithm based on PTP communication is implemented using a CSS (Chirp Spread Spectrum) communication module. A maximum transmission distance for CSS communication and an optimum moving distance interval between personal locations are then deducted and studied to estimate a fixed object location for generalization.

Method of maximum likelihood estimation of compact group objects location on CCD-frame

The urgency of developing computational methods of high-accuracy location estimation of nearby objects on a digital image is caused by a significant increase in penetrating power of optical systems. In this regard, the difficulty of achieving the required observation accuracy of nearby objects with statistically dependent images significantly increases. The paper proposes a method that allows with potential accuracy to assess the location of several nearby objects on the CCD frame. Using sub-pixel Gaussian model for object image, high measurement accuracy was achieved. The method is based on the maximum likelihood method, which reduces the computational complexity of the method. The developed method can be used in operational automated asteroids detection systems, for example, in the CoLiTec program, to assess the location of several nearby statistically dependent objects. This method is already being used in the operational automated asteroids detection system CoLiTec. The method has shown high accuracy in estimating the location of several nearby statistically dependent objects [14]. Implementation of the proposed method in CoLiTec software has increased the measurement accuracy of objects. Due to this, program users have improved their rating by the number of measurements of small solar system bodies. Namely, in 2012, among the most productive world observatories, observatories-users of CoLiTec have taken 9, 13, 22 places. According to the RMSD parameter, in pixels, objects location estimates, observatories-partners of CoLiTec for the 2012 have: ISON-NM Observatory (H15) – 0.25 pixels (5th place), Andrushevsky Astronomical Observatory (A50) - 0.23 pixels (3 place), ISON-Kislovodsk Observatory - 0.23 pixels (3rd place). Based on the average module of residuals, observatories-partners of CoLiTec also take leading positions in their class of telescopes. In 2012, this indicator was 0.061” (H15) and 0.06” (A50). So, according to this indicator, observatories H15 and A50 have taken 3 and 2 places among small aperture telescopes.

Estimating Object Location using Particle Clustering on Rotating Sonar Detection

Particle filter been used for localization and position estimation with various applications. Several method applied in order to reduce the complexity of particle information to lower processing resource requirement while providing a precise map. Sonar sensor provide a range precision with poor bearing. Partial observation applied to gain estimation of object location using several measurement from multiple vantage point. This paper propose grouping of detection particle from sonar sensor using clustering method in order to provide estimated object position from a single vantage point. The approach estimate objects using euclidean distance treshold to separate one object detections from the other and group all the detection particle into cluster containing a set of number. As a result of particle clustering, object location can be estimated with their respective weight of certainty as an attribution for further decision making.

A novel 1-dimensional object location estimation algorithm using leaky coaxial cable

A novel 1-dimensional object location estimation algorithm using leaky coaxial cable