Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments

One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors and the inability to perform a successful tracking of interest points because of the bold assumptions in VO, such as brightness constancy. We address this problem from a deep learning perspective, for which we first fine-tune a Deep Neural Network (DNN) with the purpose of obtaining enhanced representations of the sequences for VO. Then, we demonstrate how the insertion of Long Short Term Memory (LSTM) allows us to obtain temporally consistent sequences, as the estimation depends on previous states. However, the use of very deep networks does not allow the insertion into a real-time VO framework; therefore, we also propose a Convolutional Neural Network (CNN) of reduced size capable of performing faster. Finally, we validate the enhanced representations by evaluating the sequences produced by the two architectures in several state-of-art VO algorithms, such as ORB-SLAM and DSO

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

Successful visual navigation depends upon capturing images that contain sufficient useful information. In this paper, we explore a data-driven approach to account for environmental lighting changes, improving the quality of images for use in visual odometry (VO) or visual simultaneous localization and mapping (SLAM). We train a deep convolutional neural network model to predictively adjust camera gain and exposure time parameters such that consecutive images contain a maximal number of matchable features. The training process is fully self-supervised: our training signal is derived from an underlying VO or SLAM pipeline and, as a result, the model is optimized to perform well with that specific pipeline. We demonstrate through extensive real-world experiments that our network can anticipate and compensate for dramatic lighting changes (e.g., transitions into and out of road tunnels), maintaining a substantially higher number of inlier feature matches than competing camera parameter control algorithms.Comment: Accepted to IEEE Robotics and Automation Letters and to the IEEE International Conference on Robotics and Automation (ICRA) 202

Virtual Testbed for Monocular Visual Navigation of Small Unmanned Aircraft Systems

Monocular visual navigation methods have seen significant advances in the last decade, recently producing several real-time solutions for autonomously navigating small unmanned aircraft systems without relying on GPS. This is critical for military operations which may involve environments where GPS signals are degraded or denied. However, testing and comparing visual navigation algorithms remains a challenge since visual data is expensive to gather. Conducting flight tests in a virtual environment is an attractive solution prior to committing to outdoor testing. This work presents a virtual testbed for conducting simulated flight tests over real-world terrain and analyzing the real-time performance of visual navigation algorithms at 31 Hz. This tool was created to ultimately find a visual odometry algorithm appropriate for further GPS-denied navigation research on fixed-wing aircraft, even though all of the algorithms were designed for other modalities. This testbed was used to evaluate three current state-of-the-art, open-source monocular visual odometry algorithms on a fixed-wing platform: Direct Sparse Odometry, Semi-Direct Visual Odometry, and ORB-SLAM2 (with loop closures disabled)

An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras

Event cameras are ideally suited to capture High Dynamic Range (HDR) visual information without blur but provide poor imaging capability for static or slowly varying scenes. Conversely, conventional image sensors measure absolute intensity of slowly changing scenes effectively but do poorly on HDR or quickly changing scenes. In this paper, we present an asynchronous linear filter architecture, fusing event and frame camera data, for HDR video reconstruction and spatial convolution that exploits the advantages of both sensor modalities. The key idea is the introduction of a state that directly encodes the integrated or convolved image information and that is updated asynchronously as each event or each frame arrives from the camera. The state can be read-off as-often-as and whenever required to feed into subsequent vision modules for real-time robotic systems. Our experimental results are evaluated on both publicly available datasets with challenging lighting conditions and fast motions, along with a new dataset with HDR reference that we provide. The proposed AKF pipeline outperforms other state-of-the-art methods in both absolute intensity error (69.4% reduction) and image similarity indexes (average 35.5% improvement). We also demonstrate the integration of image convolution with linear spatial kernels Gaussian, Sobel, and Laplacian as an application of our architecture.Comment: 17 pages, 10 figures, Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) in August 202

Robust Visual SLAM in Challenging Environments with Low-texture and Dynamic Illumination

- Robustness to Dynamic Illumination conditions is also one of the main open challenges in visual odometry and SLAM, e.g. high dynamic range (HDR) environments. The main difficulties in these situations come from both the limitations of the sensors, for instance automatic settings of a camera might not react fast enough to properly record dynamic illumination changes, and also from limitations in the algorithms, e.g. the track of interest points is typically based on brightness constancy. The work of this thesis contributes to mitigate these phenomena from two different perspectives. The first one addresses this problem from a deep learning perspective by enhancing images to invariant and richer representations for VO and SLAM, benefiting from the generalization properties of deep neural networks. In this work it is also demonstrated how the insertion of long short term memory (LSTM) allows us to obtain temporally consistent sequences, since the estimation depends on previous states. Secondly, a more traditional perspective is exploited to contribute with a purely geometric-based tracking of line segments in challenging stereo streams with complex or varying illumination, since they are intrinsically more informative. Fecha de lectura de Tesis Doctoral: 26 de febrero 2020In the last years, visual Simultaneous Localization and Mapping (SLAM) has played a role of capital importance in rapid technological advances, e.g. mo- bile robotics and applications such as virtual, augmented, or mixed reality (VR/AR/MR), as a vital part of their processing pipelines. As its name indicates, it comprises the estimation of the state of a robot (typically the pose) while, simultaneously, incrementally building and refining a consistent representation of the environment, i.e. the so-called map, based on the equipped sensors. Despite the maturity reached by state-of-art visual SLAM techniques in controlled environments, there are still many open challenges to address be- fore reaching a SLAM system robust to long-term operations in uncontrolled scenarios, where classical assumptions, such as static environments, do not hold anymore. This thesis contributes to improve robustness of visual SLAM in harsh or difficult environments, in particular: - Low-textured Environments, where traditional approaches suffer from an accuracy impoverishment and, occasionally, the absolute failure of the system. Fortunately, many of such low-textured environments contain planar elements that are rich in linear shapes, so an alternative feature choice such as line segments would exploit information from structured parts of the scene. This set of contributions exploits both type of features, i.e. points and line segments, to produce visual odometry and SLAM algorithms robust in a broader variety of environments, hence leveraging them at all instances of the related processes: monocular depth estimation, visual odometry, keyframe selection, bundle adjustment, loop closing, etc. Additionally, an open-source C++ implementation of the proposed algorithms has been released along with the published articles and some extra multimedia material for the benefit of the community