External multi-modal imaging sensor calibration for sensor fusion: A review

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an increased demand for external sensor calibration. Despite several proven calibration solutions, they fail to fully satisfy all the evaluation criteria, including accuracy, automation, and robustness. Thus, this review aims to contribute to this growing field by examining recent research on multi-modal imaging sensor calibration and proposing future research directions. The literature review comprehensively explains the various characteristics and conditions of different multi-modal external calibration methods, including traditional motion-based calibration and feature-based calibration. Target-based calibration and targetless calibration are two types of feature-based calibration, which are discussed in detail. Furthermore, the paper highlights systematic calibration as an emerging research direction. Finally, this review concludes crucial factors for evaluating calibration methods and provides a comprehensive discussion on their applications, with the aim of providing valuable insights to guide future research directions. Future research should focus primarily on the capability of online targetless calibration and systematic multi-modal sensor calibration.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I0

Dataset of Panoramic Images for People Tracking in Service Robotics

We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility.We provide a framework for constructing a guided robot for usage in hospitals in this thesis. The omnidirectional camera on the robot allows it to recognize and track the person who is following it. Furthermore, when directing the individual to their preferred position in the hospital, the robot must be aware of its surroundings and avoid accidents with other people or items. To train and evaluate our robot's performance, we developed an auto-labeling framework for creating a dataset of panoramic videos captured by the robot's omnidirectional camera. We labeled each person in the video and their real position in the robot's frame, enabling us to evaluate the accuracy of our tracking system and guide the development of the robot's navigation algorithms. Our research expands on earlier work that has established a framework for tracking individuals using omnidirectional cameras. We want to contribute to the continuing work to enhance the precision and dependability of these tracking systems, which is essential for the creation of efficient guiding robots in healthcare facilities, by developing a benchmark dataset. Our research has the potential to improve the patient experience and increase the efficiency of healthcare institutions by reducing staff time spent guiding patients through the facility

Shaped-based IMU/Camera Tightly Coupled Object-level SLAM using Rao-Blackwellized Particle Filtering

Simultaneous Localization and Mapping (SLAM) is a decades-old problem. The classical solution to this problem utilizes entities such as feature points that cannot facilitate the interactions between a robot and its environment (e.g., grabbing objects). Recent advances in deep learning have paved the way to accurately detect objects in the image under various illumination conditions and occlusions. This led to the emergence of object-level solutions to the SLAM problem. Current object-level methods depend on an initial solution using classical approaches and assume that errors are Gaussian. This research develops a standalone solution to object-level SLAM that integrates the data from a monocular camera and an IMU (available in low-end devices) using Rao Blackwellized Particle Filter (RBPF). RBPF does not assume Gaussian distribution for the error; thus, it can handle a variety of scenarios (such as when a symmetrical object with pose ambiguities is encountered). The developed method utilizes shape instead of texture; therefore, texture-less objects can be incorporated into the solution. In the particle weighing process, a new method is developed that utilizes the Intersection over the Union (IoU) area of the observed and projected boundaries of the object that does not require point-to-point correspondence. Thus, it is not prone to false data correspondences. Landmark initialization is another important challenge for object-level SLAM. In the state-of-the-art delayed initialization, the trajectory estimation only relies on the motion model provided by IMU mechanization (during the initialization), leading to large errors. In this thesis, two novel undelayed initializations are developed. One relies only on a monocular camera and IMU, and the other utilizes an ultrasonic rangefinder as well. The developed object-level SLAM is tested using wheeled robots and handheld devices, and an error (in the position) of 4.1 to 13.1 cm (0.005 to 0.028 of the total path length) has been obtained through extensive experiments using only a single object. These experiments are conducted in different indoor environments under different conditions (e.g. illumination). Further, it is shown that undelayed initialization using an ultrasonic sensor can reduce the algorithm's runtime by half

Convex Global 3D Registration with Lagrangian Duality

The registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non-convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). This formulation renders the optimization problem independent of both the number and nature of the correspondences. The main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory. Thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Calibração de sensores do ATLASCAR2 por otimização global

In autonomous vehicles, it is often necessary to install a large number of sensors on board. Thus, the extrinsic calibration of these multi-sensory systems is a problem of high relevance for the development algorithms of autonomous driving or of assistance to the driving. This work proposes a tool to automatically calibrate simultaneously multiple cameras. In the process, aruco markers are used, which allows establishing a graph from which the geometric transformations between the various cameras and a global reference are extracted. Initially, markers are detected in the images using an OpenCV tool. Subsequently, the graph is established where the nodes are cameras or markers and the edges are the transformations between them. Then an initial estimate of the extrinsic parameters of all cameras is calculated based on the detections of the markers and the paths obtained from the graph. In the end, an optimization of the parameters is done, where the reprojection error is minimized. In order to demonstrate the process, several datasets were created in order to validate the obtained results.Em veículos autónomos é frequente a necessidade de instalar um grande número de sensores a bordo. Assim, a calibração extrínseca destes sistemas multi-sensoriais é um problema de grande relevância para o desenvolvimento de algoritmos de condução autónoma ou de apoio á condução. Este trabalho propõe um mecanismo capaz de fazer uma calibração automática em simultaâneo de várias câmaras. No processo são usados marcadores aruco, o que permite estabelecer um grafo de onde se extraem as transformações geométricas entre as várias câmaras e um referencial global. Inicialmente, os marcadores são detetados nas imagens usando uma ferramenta do OpenCV. Posteriormente é construido o grafo em que os nós são câmaras ou marcadores, e as ligações entre nós são transformações geométricas em pares câmara aruco. Em seguida é calculada uma estimativa inicial dos parâmetros extrínsecos de todas as câmaras, baseada nas deteções dos marcadores e nos caminhos obtidos do grafo. No fim, é feita uma otimização dos parâmetros, onde é minimizado o erro de reprojeção. Para demonstrar o processo foram criados vários "datasets", de modo a validar os resultados obtidos.Mestrado em Engenharia Mecânic

Extrinsic Calibration and Ego-Motion Estimation for Mobile Multi-Sensor Systems

Autonomous robots and vehicles are often equipped with multiple sensors to perform vital tasks such as localization or mapping. The joint system of various sensors with different sensing modalities can often provide better localization or mapping results than individual sensor alone in terms of accuracy or completeness. However, to enable improved performance, two important challenges have to be addressed when dealing with multi-sensor systems. Firstly, how to accurately determine the spatial relationship between individual sensor on the robot? This is a vital task known as extrinsic calibration. Without this calibration information, measurements from different sensors cannot be fused. Secondly, how to combine data from multiple sensors to correct for the deficiencies of each sensor, and thus, provides better estimations? This is another important task known as data fusion. The core of this thesis is to provide answers to these two questions. We cover, in the first part of the thesis, aspects related to improving the extrinsic calibration accuracy, and present, in the second part, novel data fusion algorithms designed to address the ego-motion estimation problem using data from a laser scanner and a monocular camera. In the extrinsic calibration part, we contribute by revealing and quantifying the relative calibration accuracies of three common types of calibration methods, so as to offer an insight into choosing the best calibration method when multiple options are available. Following that, we propose an optimization approach for solving common motion-based calibration problems. By exploiting the Gauss-Helmert model, our approach is more accurate and robust than classical least squares model. In the data fusion part, we focus on camera-laser data fusion and contribute with two new ego-motion estimation algorithms that combine complementary information from a laser scanner and a monocular camera. The first algorithm utilizes camera image information to guide the laser scan-matching. It can provide accurate motion estimates and yet can work in general conditions without requiring a field-of-view overlap between the camera and laser scanner, nor an initial guess of the motion parameters. The second algorithm combines the camera and the laser scanner information in a direct way, assuming the field-of-view overlap between the sensors is substantial. By maximizing the information usage of both the sparse laser point cloud and the dense image, the second algorithm is able to achieve state-of-the-art estimation accuracy. Experimental results confirm that both algorithms offer excellent alternatives to state-of-the-art camera-laser ego-motion estimation algorithms

A Review of Point Cloud Registration Algorithms for Mobile Robotics

International audienceThe topic of this review is geometric registration in robotics. Registration algorithms associate sets of data into a common coordinate system. They have been used extensively in object reconstruction, inspection, medical application, and localization of mobile robotics. We focus on mobile robotics applications in which point clouds are to be registered. While the underlying principle of those algorithms is simple, many variations have been proposed for many different applications. In this review, we give a historical perspective of the registration problem and show that the plethora of solutions can be organized and differentiated according to a few elements. Accordingly, we present a formalization of geometric registration and cast algorithms proposed in the literature into this framework. Finally, we review a few applications of this framework in mobile robotics that cover different kinds of platforms, environments, and tasks. These examples allow us to study the specific requirements of each use case and the necessary configuration choices leading to the registration implementation. Ultimately, the objective of this review is to provide guidelines for the choice of geometric registration configuration

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