2D cloud template matching - a comparison between iterative closest point and perfect match

Self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to algorithms accuracy, robustness and computational efficiency. In this paper we present the comparison of two of the most used map-matching algorithm, which are the Iterative Closest Point and the Perfect Match. This category of algorithms are normally applied in localization based on natural landmarks. They were compared using an extensive collection of metrics, such as accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to outliers in the robots sensors data. The test results were performed in both simulated and real world environments.info:eu-repo/semantics/publishedVersio

Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications

In this paper we present a semi-automatic 2D-3D local registration pipeline capable of coloring 3D models obtained from 3D scanners by using uncalibrated images. The proposed pipeline exploits the Structure from Motion (SfM) technique in order to reconstruct a sparse representation of the 3D object and obtain the camera parameters from image feature matches. We then coarsely register the reconstructed 3D model to the scanned one through the Scale Iterative Closest Point (SICP) algorithm. SICP provides the global scale, rotation and translation parameters, using minimal manual user intervention. In the final processing stage, a local registration refinement algorithm optimizes the color projection of the aligned photos on the 3D object removing the blurring/ghosting artefacts introduced due to small inaccuracies during the registration. The proposed pipeline is capable of handling real world cases with a range of characteristics from objects with low level geometric features to complex ones

Rekonstruktion und skalierbare Detektion und Verfolgung von 3D Objekten

The task of detecting objects in images is essential for autonomous systems to categorize, comprehend and eventually navigate or manipulate its environment. Since many applications demand not only detection of objects but also the estimation of their exact poses, 3D CAD models can prove helpful since they provide means for feature extraction and hypothesis refinement. This work, therefore, explores two paths: firstly, we will look into methods to create richly-textured and geometrically accurate models of real-life objects. Using these reconstructions as a basis, we will investigate on how to improve in the domain of 3D object detection and pose estimation, focusing especially on scalability, i.e. the problem of dealing with multiple objects simultaneously.Objekterkennung in Bildern ist für ein autonomes System von entscheidender Bedeutung, um seine Umgebung zu kategorisieren, zu erfassen und schließlich zu navigieren oder zu manipulieren. Da viele Anwendungen nicht nur die Erkennung von Objekten, sondern auch die Schätzung ihrer exakten Positionen erfordern, können sich 3D-CAD-Modelle als hilfreich erweisen, da sie Mittel zur Merkmalsextraktion und Verfeinerung von Hypothesen bereitstellen. In dieser Arbeit werden daher zwei Wege untersucht: Erstens werden wir Methoden untersuchen, um strukturreiche und geometrisch genaue Modelle realer Objekte zu erstellen. Auf der Grundlage dieser Konstruktionen werden wir untersuchen, wie sich der Bereich der 3D-Objekterkennung und der Posenschätzung verbessern lässt, wobei insbesondere die Skalierbarkeit im Vordergrund steht, d.h. das Problem der gleichzeitigen Bearbeitung mehrerer Objekte

A New Passive 3-D Automatic Target Recognition Architecture for Aerial Platforms

The 3-D automatic target recognition (ATR) has many advantages over its 2-D counterpart, but there are several constraints in the context of small low-cost unmanned aerial vehicles (UAVs). These limitations include the requirement for active rather than passive monitoring, high equipment costs, sensor packaging size, and processing burden. We, therefore, propose a new structure from motion (SfM) 3-D ATR architecture that exploits the UAV's onboard sensors, i.e., the visual band camera, gyroscope, and accelerometer, and meets the requirements of a small UAV system. We tested the proposed 3-D SfM ATR using simulated UAV reconnaissance scenarios and found that the performance was better than classic 3-D light detection and ranging (LIDAR) ATR, combining the advantages of 3-D LIDAR ATR and passive 2-D ATR. The main advantages of the proposed architecture include the rapid processing, target pose invariance, small template size, passive scene sensing, and inexpensive equipment. We implemented the SfM module under two keypoint detection, description and matching schemes, with the 3-D ATR module exploiting several current techniques. By comparing SfM 3-D ATR, 3-D LIDAR ATR, and 2-D ATR, we confirmed the superior performance of our new architecture

Featuremetric Refined Structure From Motion with a Hand-held Camera and Point Cloud Registration in Urban Scenarios

Structure from Motion (SfM), the task of recovering 3D scene structure and camera poses from 2D images or video frames, is a prominent topic in 3D Computer Vision. SfM has applications in various areas such as 3D modeling, augmented reality, robotics, and autonomous systems. Recent research has made significant improvements in the accuracy and the challenges associated with SfM. This thesis reviews and compares state-of-the-art approaches with a special focus on "Pixel-Perfect Structure-from-Motion with Featuremetric Refinement" paper. In our experiment, several videos from the city of Padova were captured using a bike-mounted camera and processed through the SfM algorithm. The generated 3D reconstructions are refined and re-evaluated after applying the aforementioned method. Next, an algorithm is developed to register the generated local point clouds with a global, georeferenced point cloud of the whole city acquired by an airplane equipped with a high-resolution LiDAR. Qualitative and quantitative experiments demonstrate promising results in generating accurate 3D reconstruction and consistent alignments between the reconstructed local point clouds and the global point cloud

3D object recognition without CAD models for industrial robot manipulation

In this work we present a new algorithm for 3D object recognition. The goal is to identify the correct position and orientation of complex objects without using a CAD model, input of main current systems. The approach we follow performs feature matching. The characteristics extracted belong only by shape information to achieve a system independent to brightness, colour or texture. Designing opportune settable parameters, we allow recognition also in presence of small deformation