Motion Estimation from Disparity Images

A new method for 3D rigid motion estimation from stereo is proposed in this paper. The appealing feature of this method is that it directly uses the disparity images obtained from stereo matching. We assume that the stereo rig has parallel cameras and show, in that case, the geometric and topological properties of the disparity images. Then we introduce a rigid transformation (called d-motion) that maps two disparity images of a rigidly moving object. We show how it is related to the Euclidean rigid motion and a motion estimation algorithm is derived. We show with experiments that our approach is simple and more accurate than standard approaches

An emprical point error model for TLS derived point clouds

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (σθ) and vertical (σα) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as σθ=±36.6 and σα=±17.8, respectively. On the other hand, a priori precision of the range observation (σρ) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as σρ=±2a'12 mm for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.This research was funded by TUBITAK - The Scientific and Technological Research Council of Turkey (Project ID: 115Y239) and by the Scientific Research Projects of Bulent Ecevit University (Project ID: 2015-47912266-01)Publisher's Versio

Optimal Computation of 3-D Similarity from Space Data with Inhomogeneous Noise Distributions

We optimally estimate the similarity (rotation, translation, and scale change) between two sets of 3-D data in the presence of inhomogeneous and anisotropic noise. Adopting the Lie algebra representation of the 3-D rotational change, we derive the Levenberg-Marquardt procedure for simultaneously optimizing the rotation, the translation, and the scale change. We test the performance of our method using simulated stereo data and real GPS geodetic sensing data. We conclude that the conventional method assuming homogeneous and isotropic noise is insufficient and that our simultaneous optimization scheme can produce an accurate solution

Registracija stereo slika postupkom zasnovanim na RANSAC strategiji s geometrijskim ograničenjem na generiranje hipoteza.

An approach for registration of sparse feature sets detected in two stereo image pairs taken from two different views is proposed. Analogously to many existing image registration approaches, our method consists of initial matching of features using local descriptors followed by a RANSAC-based procedure. The proposed approach is especially suitable for cases where there is a high percentage of false initial matches. The strategy proposed in this paper is to modify the hypothesis generation step of the basic RANSAC approach by performing a multiple-step procedure which uses geometric constraints in order to reduce the probability of false correspondences in generated hypotheses. The algorithm needs approximate information about the relative camera pose between the two views. However, the uncertainty of this information is allowed to be rather high. The presented technique is evaluated using both synthetic data and real data obtained by a stereo camera system.U radu je predložen jedan pristup registraciji skupova značajki detektiranih na dva para stereo slika snimljenih iz dva različita pogleda. Slično mnogim postojećim pristupima registraciji slika, predložena se metoda sastoji od početnog sparivanja značajki na temelju lokalnih deskriptora iza kojeg slijedi postupak temeljen na RANSAC-strategiji. Predloženi je pristup posebno prikladan za slučajeve kada rezultat početnog sparivanja sadrži veliki postotak pogrešno sparenih značajki. Strategija koja se predlaže u ovom članku je da se korak RANSAC-algoritma u kojem se slučajnim uzorkovanjem generiraju hipoteze zamijeni postupkom u kojem se hipoteza generira u više koraka, pri čemu se u svakom koraku, korištenjem odgovarajućih geometrijskih ograničenja, smanjuje vjerojatnost izbora pogrešno sparenih značajki. Algoritam treba približnu informaciju o relativnom položaju kamera između dva pogleda, pri čemu je dopuštena nesigurnost te informacije prilično velika. Predstavljena strategija je provjerena korištenjem sintetičkih podataka te pokusima sa slikama snimljenim pomoću stereo sustava kamera

Optimal Computation of 3-D Similarity: Gauss-Newton vs.Gauss-Helmert

Because 3-D data are acquired using 3-D sensing such as stereo vision and laser range finders, they have inhomogeneous and anisotropic noise. This paper studies optimal computation of the similarity (rotation, translation, and scale change) of such 3-D data. We first point out that the Gauss-Newton and the Gauss-Helmert methods, regarded as different techniques, have similar structures. We then combine them to define what we call the modified Gauss-Helmert method and do stereo vision simulation to show that it is superior to either of the two in convergence performance. Finally, we show an application to real GPS geodetic data and point out that the widely used homogeneous and isotropic noise model is insufficient and that GPS geodetic data are prone to numerical problems

Sviluppo di un algoritmo di pose estimation robusto per applicazioni aeronautiche

Il problema nella sua generalità consiste nel ricavare la stima di posizione e di orientamento di un oggetto nello spazio osservandolo con una sola telecamera, sfruttando la posizione sul piano immagine delle proiezioni di alcuni marker luminosi posti sull'oggetto stesso. Partendo dall'algoritmo OI (Orthogonal Iteration) per il calcolo della pose estimation, sono state sviluppate modifiche allo stesso per aumentare la casistica trattata e migliorarne la robustezza

Analysis of error functions for the iterative closest point algorithm

Dans les dernières années, beaucoup de progrès a été fait dans le domaine des voitures autonomes. Plusieurs grandes compagnies travaillent à créer un véhicule robuste et sûr. Pour réaliser cette tâche, ces voitures utilisent un lidar pour la localisation et pour la cartographie. Iterative Closest Point (ICP)est un algorithme de recalage de points utilisé pour la cartographie basé sur les lidars. Ce mémoire explore des approches pour améliorer le minimisateur d’erreur d’ICP. La première approche est une analyse en profondeur des filtres à données aberrantes. Quatorze des filtres les plus communs (incluant les M-estimateurs) ont été testés dans différents types d’environnement, pour un total de plus de 2 millions de recalages. Les résultats expérimentaux montrent que la plupart des filtres ont des performances similaires, s’ils sont correctement paramétrés. Néanmoins, les filtres comme Var.Trim., Cauchy et Cauchy MAD sont plus stables à travers tous les types environnements testés. La deuxième approche explore les possibilités de la cartographie à grande échelle à l’aide de lidar dans la forêt boréale. La cartographie avec un lidar est souvent basée sur des techniques de Simultaneous Localization and Mapping (SLAM) utilisant un graphe de poses, celui-ci fusionne ensemble ICP, les positions Global Navigation Satellite System (GNSS) et les mesures de l’Inertial Measurement Unit (IMU). Nous proposons une approche alternative qui fusionne ses capteurs directement dans l’étape de minimisation d’ICP. Nous avons réussi à créer une carte ayant 4.1 km de tracés de motoneige et de chemins étroits. Cette carte est localement et globalement cohérente.In recent years a lot of progress has been made in the development of self-driving cars. Multiple big companies are working on creating a safe and robust autonomous vehicle . To make this task possible, theses vehicles rely on lidar sensors for localization and mapping. Iterative Closest Point (ICP) is a registration algorithm used in lidar-based mapping. This thesis explored approaches to improve the error minimization of ICP. The first approach is an in-depth analysis of outlier filters. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. The experimental results show that most outlier filters have a similar performance if they are correctly tuned. Nonetheless, filters such as Var.Trim., Cauchy, and Cauchy MAD are more stable against different environment types. The second approach explores the possibilities of large-scale lidar mapping in a boreal forest. Lidar mapping is often based on the SLAM technique relying on pose graph optimization, which fuses the ICP algorithm, GNSS positioning, and IMU measurements. To handle those sensors directly within theICP minimization process, we propose an alternative technique of embedding external constraints. We manage to create a crisp and globally consistent map of 4.1 km of snowmobile trails and narrow walkable trails. These two approaches show how ICP can be improved through the modification of a single step of the ICP’s pipeline

Comparing Measured and Theoretical Target Registration Error of an Optical Tracking System

The goal of this thesis is to experimentally measure the accuracy of an optical tracking system used in commercial surgical navigation systems. We measure accuracy by constructing a mechanism that allows a tracked target to move with spherical motion (i.e., there exists a single point on the mechanism—the center of the sphere—that does not change position when the tracked target is moved). We imagine that the center of the sphere is the tip of a surgical tool rigidly attached to the tracked target. The location of the tool tip cannot be measured directly by the tracking system (because it is impossible to attach a tracking marker to the tool tip) and must be calculated using the measured location and orientation of the tracking target. Any measurement error in the tracking system will cause the calculated position of the tool tip to change as the target is moved; the spread of the calculated tool tip positions is a measurement of tracking error called the target registration error (TRE). The observed TRE will be compared to an analytic model of TRE to assess the predictions of the analytic model