Information-driven navigation

En los últimos años, hemos presenciado un progreso enorme de la precisión y la robustez de la “Odometría Visual” (VO) y del “Mapeo y la Localización Simultánea” (SLAM). Esta mejora de su funcionamiento ha permitido las primeras implementaciones comerciales relacionadascon la realidad aumentada (AR), la realidad virtual (VR) y la robótica. En esta tesis, desarrollamos nuevos métodos probabilísticos para mejorar la precisión, robustez y eficiencia de estas técnicas. Las contribuciones de nuestro trabajo están publicadas en tres artículos y se complementan con el lanzamiento de “SID-SLAM”, el software que contiene todas nuestras contribuciones, y del “Minimal Texture dataset”.Nuestra primera contribución es un algoritmo para la selección de puntos basado en Teoría de la Información para sistemas RGB-D VO/SLAM basados en métodos directos y/o en características visuales (features). El objetivo es seleccionar las medidas más informativas, para reducir el tama˜no del problema de optimización con un impacto mínimo en la precisión. Nuestros resultados muestran que nuestro nuevo criterio permitereducir el número de puntos hasta tan sólo 24 de ellos, alcanzando la precisión del estado del arte y reduciendo en hasta 10 veces la demanda computacional.El desarrollo de mejores modelos de incertidumbre para las medidas visuales mejoraría la precisión de la estructura y movimiento multi-vista y llevaría a estimaciones más realistas de la incertidumbre del estado en VO/SLAM. En esta tesis derivamos un modelo de covarianza para residuos multi-vista, que se convierte en un elemento crucial de nuestras contribuciones basadas en Teoría de la Información.La odometría visual y los sistemas de SLAM se dividen típicamente en la literatura en dos categorías, los basados en features y los métodos directos, dependiendo del tipo de residuos que son minimizados. En la última parte de la tesis combinamos nuestras dos contribucionesanteriores en la formulación e implementación de SID-SLAM, el primer sistema completo de SLAM semi-directo RGB-D que utiliza de forma integrada e indistinta features y métodos directos, en un sistema completo dirigido con información. Adicionalmente, grabamos ‘‘Minimal Texture”, un dataset RGB-D con un contenido visual conceptualmente simple pero arduo, con un ground truth preciso para facilitar la investigación del estado del arte en SLAM semi-directo.In the last years, we have witnessed an impressive progress in the accuracy and robustness of Visual Odometry (VO) and Simultaneous Localization and Mapping (SLAM). This boost in the performance has enabled the first commercial implementations related to augmented reality (AR), virtual reality (VR) and robotics. In this thesis, we developed new probabilistic methods to further improve the accuracy, robustness and efficiency of VO and SLAM. The contributions of our work are issued in three main publications and complemented with the release of SID-SLAM, the software containing all our contributions, and the challenging Mininal Texture dataset. Our first contribution is an information-theoretic approach to point selection for direct and/or feature-based RGB-D VO/SLAM. The aim is to select only the most informative measurements, in order to reduce the optimization problem with a minimal impact in the accuracy. Our experimental results show that our novel criteria allows us to reduce the number of tracked points down to only 24 of them, achieving state-of-the-art accuracy while reducing 10x the computational demand. Better uncertainty models for visual measurements will impact the accuracy of multi-view structure and motion and will lead to realistic uncertainty estimates of the VO/SLAM states. We derived a novel model for multi-view residual covariances based on perspective deformation, which has become a crucial element in our information-driven approach. Visual odometry and SLAM systems are typically divided in the literature into two categories, feature-based and direct methods, depending on the type of residuals that are minimized. We combined our two previous contributions in the formulation and implementation of SID-SLAM, the first full semi-direct RGB-D SLAM system that uses tightly and indistinctly features and direct methods within a complete information-driven pipeline. Moreover, we recorded Minimal Texture an RGB-D dataset with conceptually simple but challenging content, with accurate ground truth to facilitate state-of-the-art research on semi-direct SLAM.<br /

CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM. Our fusion scheme privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa. We demonstrate the use of depth prediction for estimating the absolute scale of the reconstruction, hence overcoming one of the major limitations of monocular SLAM. Finally, we propose a framework to efficiently fuse semantic labels, obtained from a single frame, with dense SLAM, yielding semantically coherent scene reconstruction from a single view. Evaluation results on two benchmark datasets show the robustness and accuracy of our approach.Comment: 10 pages, 6 figures, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, June, 2017. The first two authors contribute equally to this pape

Non-iterative RGB-D-inertial Odometry

This paper presents a non-iterative solution to RGB-D-inertial odometry system. Traditional odometry methods resort to iterative algorithms which are usually computationally expensive or require well-designed initialization. To overcome this problem, this paper proposes to combine a non-iterative front-end (odometry) with an iterative back-end (loop closure) for the RGB-D-inertial SLAM system. The main contribution lies in the novel non-iterative front-end, which leverages on inertial fusion and kernel cross-correlators (KCC) to match point clouds in frequency domain. Dominated by the fast Fourier transform (FFT), our method is only of complexity $\mathcal{O}(n\log{n})$, where $n$ is the number of points. Map fusion is conducted by element-wise operations, so that both time and space complexity are further reduced. Extensive experiments show that, due to the lightweight of the proposed front-end, the framework is able to run at a much faster speed yet still with comparable accuracy with the state-of-the-arts

Towards vision based navigation in large indoor environments

The main contribution of this paper is a novel stereo-based algorithm which serves as a tool to examine the viability of stereo vision solutions to the simultaneous localisation and mapping (SLAM) for large indoor environments. Using features extracted from the scale invariant feature transform (SIFT) and depth maps from a small vision system (SVS) stereo head, an extended Kalman fllter (EKF) based SLAM algorithm, that allows the independent use of information relating to depth and bearing, is developed. By means of a map pruning strategy for managing the computational cost, it is demonstrated that statistically consistent location estimates can be generated for a small (6 m × 6 m) structured office environment, and in a robotics search and rescue arena of similar size. It is shown that in a larger office environment, the proposed algorithm generates location estimates which are topologically correct, but statistically inconsistent. A discussion on the possible reasons for the inconsistency is presented. The paper highlights that, despite recent advances, building accurate geometric maps of large environments with vision only sensing is still a challenging task. ©2006 IEEE

SIVO: Semantically Informed Visual Odometry and Mapping

Accurate localization is a requirement for any autonomous mobile robot. In recent years, cameras have proven to be a reliable, cheap, and effective sensor to achieve this goal. Visual simultaneous localization and mapping (SLAM) algorithms determine camera motion by tracking the motion of reference points from the scene. However, these references must be static, as well as viewpoint, scale, and rotation invariant in order to ensure accurate localization. This is especially paramount for long-term robot operation, where we require our references to be stable over long durations and also require careful point selection to maintain the runtime and storage complexity of the algorithm while the robot navigates through its environment. In this thesis, we present SIVO (Semantically Informed Visual Odometry and Mapping), a novel feature selection method for visual SLAM which incorporates machine learning and neural network uncertainty into an information-theoretic approach to feature selection. The emergence of deep learning techniques has resulted in remarkable advances in scene understanding, and our method supplements traditional visual SLAM with this contextual knowledge. Our algorithm selects points which provide significant information to reduce the uncertainty of the state estimate while ensuring that the feature is detected to be a static object repeatedly, with a high confidence. This is done by evaluating the reduction in Shannon entropy between the current state entropy, and the joint entropy of the state given the addition of the new feature with the classification entropy of the feature from a Bayesian neural network. Our method is evaluated against ORB SLAM2 and the ground truth of the KITTI odometry dataset. Overall, SIVO performs comparably to ORB SLAM2 (average of 0.17% translation error difference, 6.2 × 10 −5 deg/m rotation error difference) while removing 69% of the map points on average. As the reference points selected are from static objects (building, traffic signs, etc.), the map generated using our algorithm is suitable for long-term localization

Vision-based SLAM using natural features in indoor environments

This paper presents a practical approach to solve the simultaneous localization and mapping (SLAM) problem for autonomous mobile platforms by using natural visual landmarks obtained from an stereoscopic camera. It is an attempt to depart from traditional sensors such as laser rangefinders in order to gain the many benefits of nature-inspired information-rich 3D vision sensors. Whilst this makes the system fully observable in that the sensor provide enough information (range and bearing) to compute the full 2D estate of the observed landmarks from a single position, it is also true that depth information is difficult to rely on, particularly on measurements beyond a few meters (in fact the full 3D estate is observable, but here robot motion is constrained to 2D and only the 2D problem is considered). The work presented here is an attempt to overcome such a drawback by tackling the problem from a partially measurable SLAM perspective in that only landmark bearing from one of the cameras is employed in the fusion estimation. Range information estimates from the stereo pair is only used during map building in the landmark initialization phase in order to provide a reasonably accurate initial estimate. An additional benefit of the approach presented here lies in the data association aspect of SLAM. The availability of powerful feature extraction algorithms from the vision community, such as SIFT, permits a more flexible SLAM implementation separated from feature representation, extraction and matching, essentially carrying out matching with minimal recourse to geometry. Simulation results on real data illustrate the validity of the approach. © 2005 IEEE

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.Comment: submitted to IROS 201