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

Absolute depth using low-cost light field cameras

Digital cameras are increasingly used for measurement tasks within engineering scenarios, often being part of metrology platforms. Existing cameras are well equipped to provide 2D information about the fields of view (FOV) they observe, the objects within the FOV, and the accompanying environments. But for some applications these 2D results are not sufficient, specifically applications that require Z dimensional data (depth data) along with the X and Y dimensional data. New designs of camera systems have previously been developed by integrating multiple cameras to provide 3D data, ranging from 2 camera photogrammetry to multiple camera stereo systems. Many earlier attempts to record 3D data on 2D sensors have been completed, and likewise many research groups around the world are currently working on camera technology but from different perspectives; computer vision, algorithm development, metrology, etc. Plenoptic or Lightfield camera technology was defined as a technique over 100 years ago but has remained dormant as a potential metrology instrument. Lightfield cameras utilize an additional Micro Lens Array (MLA) in front of the imaging sensor, to create multiple viewpoints of the same scene and allow encoding of depth information. A small number of companies have explored the potential of lightfield cameras, but in the majority, these have been aimed at domestic consumer photography, only ever recording scenes as relative scale greyscale images. This research considers the potential for lightfield cameras to be used for world scene metrology applications, specifically to record absolute coordinate data. Specific interest has been paid to a range of low cost lightfield cameras to; understand the functional/behavioural characteristics of the optics, identify potential need for optical and/or algorithm development, define sensitivity, repeatability and accuracy characteristics and limiting thresholds of use, and allow quantified 3D absolute scale coordinate data to be extracted from the images. The novel output of this work is; an analysis of lightfield camera system sensitivity leading to the definition of Active Zones (linear data generation good data) and In-active Zones (non-linear data generation poor data), development of bespoke calibration algorithms that remove radial/tangential distortion from the data captured using any MLA based camera, and, a light field camera independent algorithm that allows the delivery of 3D coordinate data in absolute units within a well-defined measurable range from a given camera

From Calibration to Large-Scale Structure from Motion with Light Fields

Classic pinhole cameras project the multi-dimensional information of the light flowing through a scene onto a single 2D snapshot. This projection limits the information that can be reconstructed from the 2D acquisition. Plenoptic (or light field) cameras, on the other hand, capture a 4D slice of the plenoptic function, termed the “light field”. These cameras provide both spatial and angular information on the light flowing through a scene; multiple views are captured in a single photographic exposure facilitating various applications. This thesis is concerned with the modelling of light field (or plenoptic) cameras and the development of structure from motion pipelines using such cameras. Specifically, we develop a geometric model for a multi-focus plenoptic camera, followed by a complete pipeline for the calibration of the suggested model. Given a calibrated light field camera, we then remap the captured light field to a grid of pinhole images. We use these images to obtain metric 3D reconstruction through a novel framework for structure from motion with light fields. Finally, we suggest a linear and efficient approach for absolute pose estimation for light fields

Camera Calibration with Non-Central Local Camera Models

Kamerakalibrierung ist eine wichtige Grundvoraussetzung für viele Computer-Vision-Algorithmen wie Stereo-Vision und visuelle Odometrie. Das Ziel der Kamerakalibrierung besteht darin, sowohl die örtliche Lage der Kameras als auch deren Abbildungsmodell zu bestimmen. Das Abbildungsmodell einer Kamera beschreibt den Zusammenhang zwischen der 3D-Welt und der Bildebene. Aktuell werden häufig einfache globale Kamera-Modelle in einem Kalibrierprozess geschätzt, welcher mit vergleichsweise geringem Aufwand und einer großen Fehlertoleranz durchgeführt werden kann. Um das resultierende Kameramodell zu bewerten, wird in der Regel der Rückprojektionsfehler als Maß herangezogen. Jedoch können auch einfache Kameramodelle, die das Abbildungsverhalten von optischen Systemen nicht präzise beschreiben können, niedrige Rückprojektionsfehler erzielen. Dies führt dazu, dass immer wieder schlecht kalibrierte Kameramodelle nicht als solche identifiziert werden. Um dem entgegen zu wirken, wird in dieser Arbeit ein neues kontinuierliches nicht-zentrales Kameramodell basierend auf B-Splines vorgeschlagen. Dieses Abbildungsmodell ermöglicht es, verschiedene Objektive und nicht-zentrale Verschiebungen, die zum Beispiel durch eine Platzierung der Kamera hinter einer Windschutzscheibe entstehen, akkurat abzubilden. Trotz der allgemeinen Modellierung kann dieses Kameramodell durch einen einfach zu verwendenden Schachbrett-Kalibrierprozess geschätzt werden. Um Kalibrierergebnisse zu bewerten, wird anstelle des mittleren Rückprojektionsfehlers ein Kalibrier-Benchmark vorgeschlagen. Die Grundwahrheit des Kameramodells wird durch ein diskretes Sichtstrahlen-basiertes Modell beschrieben. Um dieses Modell zu schätzen, wird ein Kalibrierprozess vorgestellt, welches ein aktives Display als Ziel verwendet. Dabei wird eine lokale Parametrisierung für die Sichtstrahlen vorgestellt und ein Weg aufgezeigt, die Oberfläche des Displays zusammen mit den intrinsischen Kameraparametern zu schätzen. Durch die Schätzung der Oberfläche wird der mittlere Punkt-zu-Linien-Abstand um einen Faktor von mehr als 20 reduziert. Erst dadurch kann das so geschätzte Kameramodell als Grundwahrheit dienen. Das vorgeschlagene Kameramodell und die dazugehörigen Kalibrierprozesse werden durch eine ausführliche Auswertung in Simulation und in der echten Welt mithilfe des neuen Kalibrier-Benchmarks bewertet. Es wird gezeigt, dass selbst in dem vereinfachten Fall einer ebenen Glasscheibe, die vor der Kamera platziert ist, das vorgeschlagene Modell sowohl einem zentralen als auch einem nicht-zentralen globalen Kameramodell überlegen ist. Am Ende wird die Praxistauglichkeit des vorgeschlagenen Modells bewiesen, indem ein automatisches Fahrzeug kalibriert wird, das mit sechs Kameras ausgestattet ist, welche in unterschiedliche Richtungen zeigen. Der mittlere Rückprojektionsfehler verringert sich durch das neue Modell bei allen Kameras um den Faktor zwei bis drei. Der Kalibrier-Benchmark ermöglicht es in Zukunft, die Ergebnisse verschiedener Kalibrierverfahren miteinander zu vergleichen und die Genauigkeit des geschätzten Kameramodells mithilfe der Grundwahrheit akkurat zu bestimmen. Die Verringerung des Kalibrierfehlers durch das neue vorgeschlagene Kameramodell hilft die Genauigkeit weiterführender Algorithmen wie Stereo-Vision, visuelle Odometrie oder 3D-Rekonstruktion zu erhöhen

Automatic Extrinsic Calibration of Vision and Lidar by Maximizing Mutual Information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112212/1/rob21542.pd

Optimal Image-Aided Inertial Navigation

The utilization of cameras in integrated navigation systems is among the most recent scientific research and high-tech industry development. The research is motivated by the requirement of calibrating off-the-shelf cameras and the fusion of imaging and inertial sensors in poor GNSS environments. The three major contributions of this dissertation are The development of a structureless camera auto-calibration and system calibration algorithm for a GNSS, IMU and stereo camera system. The auto-calibration bundle adjustment utilizes the scale restraint equation, which is free of object coordinates. The number of parameters to be estimated is significantly reduced in comparison with the ones in a self-calibrating bundle adjustment based on the collinearity equations. Therefore, the proposed method is computationally more efficient. The development of a loosely-coupled visual odometry aided inertial navigation algorithm. The fusion of the two sensors is usually performed using a Kalman filter. The pose changes are pairwise time-correlated, i.e. the measurement noise vector at the current epoch is only correlated with the one from the previous epoch. Time-correlated errors are usually modelled by a shaping filter. The shaping filter developed in this dissertation uses Cholesky factors as coefficients derived from the variance and covariance matrices of the measurement noise vectors. Test results with showed that the proposed algorithm performs better than the existing ones and provides more realistic covariance estimates. The development of a tightly-coupled stereo multi-frame aided inertial navigation algorithm for reducing position and orientation drifts. Usually, the image aiding based on the visual odometry uses the tracked features only from a pair of the consecutive image frames. The proposed method integrates the features tracked from multiple overlapped image frames for reducing the position and orientation drifts. The measurement equation is derived from SLAM measurement equation system where the landmark positions in SLAM are algebraically by time-differencing. However, the derived measurements are time-correlated. Through a sequential de-correlation, the Kalman filter measurement update can be performed sequentially and optimally. The main advantages of the proposed algorithm are the reduction of computational requirements when compared to SLAM and a seamless integration into an existing GNSS aided-IMU system

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications

Plenoptic Signal Processing for Robust Vision in Field Robotics

This thesis proposes the use of plenoptic cameras for improving the robustness and simplicity of machine vision in field robotics applications. Dust, rain, fog, snow, murky water and insufficient light can cause even the most sophisticated vision systems to fail. Plenoptic cameras offer an appealing alternative to conventional imagery by gathering significantly more light over a wider depth of field, and capturing a rich 4D light field structure that encodes textural and geometric information. The key contributions of this work lie in exploring the properties of plenoptic signals and developing algorithms for exploiting them. It lays the groundwork for the deployment of plenoptic cameras in field robotics by establishing a decoding, calibration and rectification scheme appropriate to compact, lenslet-based devices. Next, the frequency-domain shape of plenoptic signals is elaborated and exploited by constructing a filter which focuses over a wide depth of field rather than at a single depth. This filter is shown to reject noise, improving contrast in low light and through attenuating media, while mitigating occluders such as snow, rain and underwater particulate matter. Next, a closed-form generalization of optical flow is presented which directly estimates camera motion from first-order derivatives. An elegant adaptation of this "plenoptic flow" to lenslet-based imagery is demonstrated, as well as a simple, additive method for rendering novel views. Finally, the isolation of dynamic elements from a static background is considered, a task complicated by the non-uniform apparent motion caused by a mobile camera. Two elegant closed-form solutions are presented dealing with monocular time-series and light field image pairs. This work emphasizes non-iterative, noise-tolerant, closed-form, linear methods with predictable and constant runtimes, making them suitable for real-time embedded implementation in field robotics applications