644 research outputs found
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in
CNNs - is of increasing interest to the vision community. Unsupervised
strategies to learning are particularly appealing as they can utilize much
larger and varied monocular video datasets during learning without the need for
ground truth depth or stereo. In previous works, separate pose and depth CNN
predictors had to be determined such that their joint outputs minimized the
photometric error. Inspired by recent advances in direct visual odometry (DVO),
we argue that the depth CNN predictor can be learned without a pose CNN
predictor. Further, we demonstrate empirically that incorporation of a
differentiable implementation of DVO, along with a novel depth normalization
strategy - substantially improves performance over state of the art that use
monocular videos for training
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
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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
An Experimental Comparison of Monocular and Stereo Visual FastSLAM Implementations
Implementazione e confronto di FastSLAM monoculare e stereo evidenziando le differenti performances riguardo a mappatura e stima della traiettoria. E' inoltre proposto un algoritmo integrante Visual Odometry e SLAM da stereovisione per robot dotati di sole fotocamere come sensoriope
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