Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

Event-Based Visual-Inertial Odometry Using Smart Features

Event-based cameras are a novel type of visual sensor that operate under a unique paradigm, providing asynchronous data on the log-level changes in light intensity for individual pixels. This hardware-level approach to change detection allows these cameras to achieve ultra-wide dynamic range and high temporal resolution. Furthermore, the advent of convolutional neural networks (CNNs) has led to state-of-the-art navigation solutions that now rival or even surpass human engineered algorithms. The advantages offered by event cameras and CNNs make them excellent tools for visual odometry (VO). This document presents the implementation of a CNN trained to detect and describe features within an image as well as the implementation of an event-based visual-inertial odometry (EVIO) pipeline, which estimates a vehicle\u27s 6-degrees-offreedom (DOF) pose using an affixed event-based camera with an integrated inertial measurement unit (IMU). The front-end of this pipeline utilizes a neural network for generating image frames from asynchronous event camera data. These frames are fed into a multi-state constraint Kalman filter (MSCKF) back-end that uses the output of the developed CNN to perform measurement updates. The EVIO pipeline was tested on a selection from the Event-Camera Dataset [1], and on a dataset collected from a fixed-wing unmanned aerial vehicle (UAV) flight test conducted by the Autonomy and Navigation Technology (ANT) Center

Learning and Searching Methods for Robust, Real-Time Visual Odometry.

Accurate position estimation provides a critical foundation for mobile robot perception and control. While well-studied, it remains difficult to provide timely, precise, and robust position estimates for applications that operate in uncontrolled environments, such as robotic exploration and autonomous driving. Continuous, high-rate egomotion estimation is possible using cameras and Visual Odometry (VO), which tracks the movement of sparse scene content known as image keypoints or features. However, high update rates, often 30~Hz or greater, leave little computation time per frame, while variability in scene content stresses robustness. Due to these challenges, implementing an accurate and robust visual odometry system remains difficult. This thesis investigates fundamental improvements throughout all stages of a visual odometry system, and has three primary contributions: The first contribution is a machine learning method for feature detector design. This method considers end-to-end motion estimation accuracy during learning. Consequently, accuracy and robustness are improved across multiple challenging datasets in comparison to state of the art alternatives. The second contribution is a proposed feature descriptor, TailoredBRIEF, that builds upon recent advances in the field in fast, low-memory descriptor extraction and matching. TailoredBRIEF is an in-situ descriptor learning method that improves feature matching accuracy by efficiently customizing descriptor structures on a per-feature basis. Further, a common asymmetry in vision system design between reference and query images is described and exploited, enabling approaches that would otherwise exceed runtime constraints. The final contribution is a new algorithm for visual motion estimation: Perspective Alignment Search~(PAS). Many vision systems depend on the unique appearance of features during matching, despite a large quantity of non-unique features in otherwise barren environments. A search-based method, PAS, is proposed to employ features that lack unique appearance through descriptorless matching. This method simplifies visual odometry pipelines, defining one method that subsumes feature matching, outlier rejection, and motion estimation. Throughout this work, evaluations of the proposed methods and systems are carried out on ground-truth datasets, often generated with custom experimental platforms in challenging environments. Particular focus is placed on preserving runtimes compatible with real-time operation, as is necessary for deployment in the field.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113365/1/chardson_1.pd

Visual Perception for Manipulation and Imitation in Humanoid Robots

This thesis deals with visual perception for manipulation and imitation in humanoid robots. In particular, real-time applicable methods for object recognition and pose estimation as well as for markerless human motion capture have been developed. As only sensor a small baseline stereo camera system (approx. human eye distance) was used. An extensive experimental evaluation has been performed on simulated as well as real image data from real-world scenarios using the humanoid robot ARMAR-III

Theory, Design, and Implementation of Landmark Promotion Cooperative Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) is a challenging problem in practice, the use of multiple robots and inexpensive sensors poses even more demands on the designer. Cooperative SLAM poses specific challenges in the areas of computational efficiency, software/network performance, and robustness to errors. New methods in image processing, recursive filtering, and SLAM have been developed to implement practical algorithms for cooperative SLAM on a set of inexpensive robots. The Consolidated Unscented Mixed Recursive Filter (CUMRF) is designed to handle non-linear systems with non-Gaussian noise. This is accomplished using the Unscented Transform combined with Gaussian Mixture Models. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis (PCA) and the X84 outlier rejection rule. Forgetful SLAM is a local SLAM technique that runs in nearly constant time relative to the number of visible landmarks and improves poor performing sensors through sensor fusion and outlier rejection. Forgetful SLAM correlates all measured observations, but stops the state from growing over time. Hierarchical Active Ripple SLAM (HAR-SLAM) is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple robots, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of robots, landmarks, and robots poses. This dissertation presents explicit methods for closing-the-loop, joining multiple robots, and active updates. Landmark Promotion SLAM is a hierarchy of new SLAM methods, using the Robust Kalman Filter, Forgetful SLAM, and HAR-SLAM. Practical aspects of SLAM are a focus of this dissertation. LK-SURF is a new image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking. Typical stereo correspondence techniques fail at providing descriptors for features, or fail at temporal tracking. Several calibration and modeling techniques are also covered, including calibrating stereo cameras, aligning stereo cameras to an inertial system, and making neural net system models. These methods are important to improve the quality of the data and images acquired for the SLAM process

Aggregating Local Features into Bundles for High-Precision Object Retrieval

Due to the omnipresence of digital cameras and mobile phones the number of images stored in image databases has grown tremendously in the last years. It becomes apparent that new data management and retrieval techniques are needed to deal with increasingly large image databases. This thesis presents new techniques for content-based image retrieval where the image content itself is used to retrieve images by visual similarity from databases. We focus on the query-by-example scenario, assuming the image itself is provided as query to the retrieval engine. In many image databases, images are often associated with metadata, which may be exploited to improve the retrieval performance. In this work, we present a technique that fuses cues from the visual domain and textual annotations into a single compact representation. This combined multimodal representation performs significantly better compared to the underlying unimodal representations, which we demonstrate on two large-scale image databases consisting of up to 10 million images. The main focus of this work is on feature bundling for object retrieval and logo recognition. We present two novel feature bundling techniques that aggregate multiple local features into a single visual description. In contrast to many other works, both approaches encode geometric information about the spatial layout of local features into the corresponding visual description itself. Therefore, these descriptions are highly distinctive and suitable for high-precision object retrieval. We demonstrate the use of both bundling techniques for logo recognition. Here, the recognition is performed by the retrieval of visually similar images from a database of reference images, making the recognition systems easily scalable to a large number of classes. The results show that our retrieval-based methods can successfully identify small objects such as logos with an extremely low false positive rate. In particular, our feature bundling techniques are beneficial because false positives are effectively avoided upfront due to the highly distinctive descriptions. We further demonstrate and thoroughly evaluate the use of our bundling technique based on min-Hashing for image and object retrieval. Compared to approaches based on conventional bag-of-words retrieval, it has much higher efficiency: the retrieved result lists are shorter and cleaner while recall is on equal level. The results suggest that this bundling scheme may act as pre-filtering step in a wide range of scenarios and underline the high effectiveness of this approach. Finally, we present a new variant for extremely fast re-ranking of retrieval results, which ranks the retrieved images according to the spatial consistency of their local features to those of the query image. The demonstrated method is robust to outliers, performs better than existing methods and allows to process several hundreds to thousands of images per second on a single thread