High-Performance and Tunable Stereo Reconstruction

Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Our key contribution is a disparity estimation algorithm that iteratively approximates the scene depth via a piece-wise planar mesh from stereo imagery, with a fast depth validation step for semi-dense reconstruction. The mesh is initially seeded with sparsely matched keypoints, and is recursively tessellated and refined as needed (via a resampling stage), to provide the desired stereo disparity accuracy. The inherent simplicity and speed of our approach, with the ability to tune it to a desired reconstruction quality and runtime performance makes it a compelling solution for applications in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation (ICRA) 2016; 8 pages, 5 figure

A New Feature Detector and Stereo Matching Method for Accurate High-Performance Sparse Stereo Matching

Abstract — Hardware platforms with limited processing power are often incapable of running dense stereo analysis algorithms at acceptable speed. Sparse algorithms provide an alternative but generally lack in accuracy. To overcome this predicament, we present an efficient sparse stereo analysis algorithm that applies a dense consistency check, leading to accurate matching results. We further improve matching accuracy by introducing a new feature detector based on FAST, which exhibits a less clustered feature distribution. The new feature detector leads to a superior performance of our stereo analysis algorithm. Performance evaluation shows that the proposed stereo matching system achieves processing rates above 200 frames per second on a commodity dual core CPU, and faster than video frame-rate processing on a lowperformance embedded platform. The stereo matching results prove to be superior to those obtained with ordinary sparse matching algorithms. I

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

Development of an augmented reality guided computer assisted orthopaedic surgery system

Previously held under moratorium from 1st December 2016 until 1st December 2021.This body of work documents the developed of a proof of concept augmented reality guided computer assisted orthopaedic surgery system – ARgCAOS. After initial investigation a visible-spectrum single camera tool-mounted tracking system based upon fiducial planar markers was implemented. The use of visible-spectrum cameras, as opposed to the infra-red cameras typically used by surgical tracking systems, allowed the captured image to be streamed to a display in an intelligible fashion. The tracking information defined the location of physical objects relative to the camera. Therefore, this information allowed virtual models to be overlaid onto the camera image. This produced a convincing augmented experience, whereby the virtual objects appeared to be within the physical world, moving with both the camera and markers as expected of physical objects. Analysis of the first generation system identified both accuracy and graphical inadequacies, prompting the development of a second generation system. This too was based upon a tool-mounted fiducial marker system, and improved performance to near-millimetre probing accuracy. A resection system was incorporated into the system, and utilising the tracking information controlled resection was performed, producing sub-millimetre accuracies. Several complications resulted from the tool-mounted approach. Therefore, a third generation system was developed. This final generation deployed a stereoscopic visible-spectrum camera system affixed to a head-mounted display worn by the user. The system allowed the augmentation of the natural view of the user, providing convincing and immersive three dimensional augmented guidance, with probing and resection accuracies of 0.55±0.04 and 0.34±0.04 mm, respectively.This body of work documents the developed of a proof of concept augmented reality guided computer assisted orthopaedic surgery system – ARgCAOS. After initial investigation a visible-spectrum single camera tool-mounted tracking system based upon fiducial planar markers was implemented. The use of visible-spectrum cameras, as opposed to the infra-red cameras typically used by surgical tracking systems, allowed the captured image to be streamed to a display in an intelligible fashion. The tracking information defined the location of physical objects relative to the camera. Therefore, this information allowed virtual models to be overlaid onto the camera image. This produced a convincing augmented experience, whereby the virtual objects appeared to be within the physical world, moving with both the camera and markers as expected of physical objects. Analysis of the first generation system identified both accuracy and graphical inadequacies, prompting the development of a second generation system. This too was based upon a tool-mounted fiducial marker system, and improved performance to near-millimetre probing accuracy. A resection system was incorporated into the system, and utilising the tracking information controlled resection was performed, producing sub-millimetre accuracies. Several complications resulted from the tool-mounted approach. Therefore, a third generation system was developed. This final generation deployed a stereoscopic visible-spectrum camera system affixed to a head-mounted display worn by the user. The system allowed the augmentation of the natural view of the user, providing convincing and immersive three dimensional augmented guidance, with probing and resection accuracies of 0.55±0.04 and 0.34±0.04 mm, respectively