Impact of different trajectories on extrinsic self-calibration for vehicle-based mobile laser scanning systems

The trend toward further integration of automotive electronic control units functionality into domain control units as well as the rise of computing-intensive driver assistance systems has led to a demand for high-performance automotive computation platforms. These platforms have to fulfill stringent safety requirements. One promising approach is the use of performance computation units in combination with safety controllers in a single control unit. Such systems require adequate communication links between the computation units. While Ethernet is widely used, a high-speed serial link communication protocol supported by an Infineon AURIX safety controller appears to be a promising alternative. In this paper, a high-speed serial link IP core is presented, which enables this type of high-speed serial link communication interface for field-programmable gate array–based computing units. In our test setup, the IP core was implemented in a high-performance Xilinx Zynq UltraScale+, which communicated with an Infineon AURIX via high-speed serial link and Ethernet. The first bandwidth measurements demonstrated that high-speed serial link is an interesting candidate for inter-chip communication, resulting in bandwidths reaching up to 127 Mbit/s using stream transmissions

Calibration and 3D Model Generation for a Low-Cost Structured Light Foot Scanner

The need for custom footwear among the consumers is growing every day. Serious research is being undertaken with regards to the fit and comfort of the footwear. The integration of scanning systems in the footwear and orthotic industries have played a significant role in generating 3D digital representation of the foot for automated measurements from which a custom footwear or an orthosis is manufactured. The cost of such systems is considerably high for many manufacturers due to their expensive components, complex processing algorithms and difficult calibration techniques. This thesis presents a fast and robust calibration technique for a low-cost 3D laser scanner. The calibration technique is based on determining the mathematical relationship that relates the image coordinates to the real world coordinates. The relationship is determined by mapping the known real world coordinates of a reference object to its corresponding image coordinates by multivariate polynomial regression. With the developed mathematical relationship, 3D data points can be obtained from the 2D images of any object placed in the scanner. An image processing script is developed to detect the 2D image points of the laser profile in a series of scan images from 8 cameras. The detected 2D image points are reconstructed into 3D data points based on the mathematical model developed by the calibration process. Following that, the output model is achieved by triangulating the 3D data points as a mesh model with vertices and normals. The data is exported as a computer aided design (CAD) software readable format for viewing and measuring. This method proves to be less complex and the scanner was able to generate 3D models with an accuracy of +/-0.05 cm. The 3D data points from the output model were compared against a reference model scanned by an industrial grade scanner to verify and validate the result. The devised methodology for calibrating the 3D laser scanner can be employed to obtain accurate and reliable 3D data of the foot shape and it has been successfully tested with several participants

Robot guidance using machine vision techniques in industrial environments: A comparative review

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works

Toward Global Sensing Quality Maximization: A Configuration Optimization Scheme for Camera Networks

The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized globally and simultaneously. We first propose to use the number of pixels occupied by a unit-length object in image as a metric of the sensing quality of the object, which is determined by the parameters of the camera, such as intrinsic, extrinsic, and distortional coefficients. Then, we form a single quantity that measures the sensing quality of the targets by the camera network. This quantity further serves as the objective function of our optimization problem to obtain the optimal camera configuration. We verify the effectiveness of our approach through extensive simulations and experiments, and the results reveal its improved performance on the AprilTag detection tasks. Codes and related utilities for this work are open-sourced and available at https://github.com/sszxc/MultiCam-Simulation.Comment: The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Application for photogrammetry of organisms

Single-camera photogrammetry is a well-established procedure to retrieve quantitative information from objects using photography. In biological sciences, photogrammetry is often applied to aid in morphometry studies, focusing on the comparative study of shapes and organisms. Two types of photogrammetry are used in morphometric studies: 2D photogrammetry, where distance and angle measurements are used to quantitatively describe attributes of an object, and 3D photogrammetry, where data on landmark coordinates are used to reconstruct an object true shape. Although there are excellent software tools for 3D photogrammetry available, software specifically designed to aid in the somewhat simpler 2D photogrammetry are lacking. Therefore, most studies applying 2D photogrammetry, still rely on manual acquisition of measurements from pictures, that must then be scaled to an appropriate measuring system. This is often a laborious multistep process, on most cases utilizing diverse software to complete different tasks. In addition to being time-consuming, it is also error-prone since measurement recording is often made manually. The present work aimed at tackling those issues by implementing a new cross-platform software able to integrate and streamline the photogrammetry workflow usually applied in 2D photogrammetry studies. Results from a preliminary study show a decrease of 45% in processing time when using the software developed in the scope of this work in comparison with a competing methodology. Existing limitations and future work towards improved versions of the software are discussed.Fotogrametria em câmera única é um procedimento bem estabelecido para recolher dados quantitativos de objectos através de fotografias. Em biologia, fotogrametria é frequentemente aplicada no contexto de estudos morfométricos, focando-se no estudo comparativo de formas e organismos. Nos estudos morfométricos são utilizados dois tipos de aplicação fotogramétrica: fotogrametria 2D, onde são utilizadas medidas de distância e ângulo para quantitativamente descrever atributos de um objecto, e fotogrametria 3D, onde são utilizadas coordenadas de referência de forma a reconstruir a verdadeira forma de um objeto. Apesar da existência de uma elevada variedade de software no contexto de fotogrametria 3D, a variedade de software concebida especificamente para a a aplicação de fotogrametria 2D é ainda muito reduzida. Consequentemente, é comum observar estudos onde fotogrametria 2D é utilizada através da aquisição manual de medidas a partir de imagens, que posteriormente necessitam de ser escaladas para um sistema apropriado de medida. Este processo de várias etapas é frequentemente moroso e requer a aplicação de diferentes programas de software. Além de ser moroso, é também susceptível a erros, dada a natureza manual na aquisição de dados. O presente trabalho visou abordar os problemas descritos através da implementação de um novo software multiplataforma capaz de integrar e agilizar o processo de fotogrametria presentes em estudos que requerem fotogrametria 2D. Resultados preliminares demonstram um decréscimo de 45% em tempo de processamento na utilização do software desenvolvido no âmbito deste trabalho quando comparado a uma metodologia concorrente. Limitações existentes e trabalho futuro são discutidos

On the Development of a Generic Multi-Sensor Fusion Framework for Robust Odometry Estimation

In this work we review the design choices, the mathematical and software engineering techniques employed in the development of the ROAMFREE sensor fusion library, a general, open-source framework for pose tracking and sensor parameter self-calibration in mobile robotics. In ROAMFREE, a comprehensive logical sensor library allows to abstract from the actual sensor hardware and processing while preserving model accuracy thanks to a rich set of calibration parameters, such as biases, gains, distortion matrices and geometric placement dimensions. The modular formulation of the sensor fusion problem, which is based on state-of-the-art factor graph inference techniques, allows to handle arbitrary number of multi-rate sensors and to adapt to virtually any kind of mobile robot platform, such as Ackerman steering vehicles, quadrotor unmanned aerial vehicles, omni-directional mobile robots. Different solvers are available to target high-rate online pose tracking tasks and offline accurate trajectory smoothing and parameter calibration. The modularity, versatility and out-of-the-box functioning of the resulting framework came at the cost of an increased complexity of the software architecture, with respect to an ad-hoc implementation of a platform dependent sensor fusion algorithm, and required careful design of abstraction layers and decoupling interfaces between solvers, state variables representations and sensor error models. However, we review how a high level, clean, C++/Python API, as long as ROS interface nodes, hide the complexity of sensor fusion tasks to the end user, making ROAMFREE an ideal choice for new, and existing, mobile robot projects

3D high resolution techniques applied on small and medium size objects: from the analysis of the process towards quality assessment

The need for metric data acquisition is an issue strictly related to the human capability of describing the world with rigorous and repeatable methods. From the invention of photography to the development of advanced computers, the metric data acquisition has been subjected to rapid mutation, and nowadays there exists a strict connection between metric data acquisition and image processing, Computer Vision and Artificial Intelligence. The sensor devices for the 3D model generation are various and characterized by different functioning principles. In this work, optical passive and active sensors are treated, focusing specifically on close-range photogrammetry, Time of Flight (ToF) sensors and Structured-light scanners (SLS). Starting from the functioning principles of the techniques and showing some issues related to them, the work highlights their potentialities, analyzing the fundamental and most critical steps of the process leading to the quality assessment of the data. Central themes are the instruments calibration, the acquisition plan and the interpretation of the final results. The capability of the acquisition techniques to satisfy unconventional requirements in the field of Cultural Heritage is also shown. The thesis starts with an overview about the history and developments of 3D metric data acquisition. Chapter 1 treats the Human Vision System and presents a complete overview of 3D sensing devices. Chapter 2 starts from the enunciation of the basic principle of close-range photogrammetry considering digital cameras functioning principles, calibration issues, and the process leading to the 3D mesh reconstruction. The case of multi-image acquisition is analyzed, deepening the quality assessment of the photogrammetric process through a case study. Chapter 3 is devoted to the range-based acquisition techniques, namely ToF laser scanners and SLSs. Lastly, Chapter 4 focuses on unconventional applications of the mentioned high-resolution acquisition techniques showing some examples of study cases in the field of Cultural Heritage

Visual Perception For Robotic Spatial Understanding

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don\u27t have off-the-shelf libraries for this capability. Why is this? The simple answer is that the problem is extremely difficult. There has been progress, but the current state of the art is impressive and depressing at the same time. We now have neural networks that can recognize many objects in 2D images, in some cases performing better than a human. Some algorithms can also provide bounding boxes or pixel-level masks to localize the object. We have visual odometry and mapping algorithms that can build reasonably detailed maps over long distances with the right hardware and conditions. On the other hand, we have robots with many sensors and no efficient way to compute their relative extrinsic poses for integrating the data in a single frame. The same networks that produce good object segmentations and labels in a controlled benchmark still miss obvious objects in the real world and have no mechanism for learning on the fly while the robot is exploring. Finally, while we can detect pose for very specific objects, we don\u27t yet have a mechanism that detects pose that generalizes well over categories or that can describe new objects efficiently. We contribute algorithms in four of the areas mentioned above. First, we describe a practical and effective system for calibrating many sensors on a robot with up to 3 different modalities. Second, we present our approach to visual odometry and mapping that exploits the unique capabilities of RGB-D sensors to efficiently build detailed representations of an environment. Third, we describe a 3-D over-segmentation technique that utilizes the models and ego-motion output in the previous step to generate temporally consistent segmentations with camera motion. Finally, we develop a synthesized dataset of chair objects with part labels and investigate the influence of parts on RGB-D based object pose recognition using a novel network architecture we call PartNet