Perception-aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This paper proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using 4D-BIM, considering the project progress, safety requirements, and UAV's localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.Comment: [Final draft] This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers and the Journal of Computing in Civil Engineerin

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 156)

This bibliography lists 170 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1976

Semantic depth estimation with monocular camera for autonomous navigation of small unmanned aircraft

Demand for small Unmanned Aircraft (UA) applications in Global Navigation Satellite System (GNSS) denied environment has increased over the years in areas such as internal building infrastructure inspection, indoor security surveillance and stock cycle counting. One of the key challenges in the current development of autonomous UA is the localization and pose estimation in the absence of GNSS signals. Various methods using onboard sensors such as Light Detection and Ranging (LiDAR) have been adopted but with the compromise of take-off weight and computing complexity. Off-board sensors such as motion trackers or Radio Frequency (RF) based beacons have also been adopted but are costly and limited to a small area of operations within the sensor’s range. With the advancement of computer vision and deep neural networks, and the fact that the majority of consumer and commercial UA comes equipped with high resolution cameras, it is now even more possible to exploit camera images for navigational tasks. To enhance the accuracy of traditional computer vision methods, machine learning can be adopted to model complex image variations for more accurate predictions. In this thesis, a novel approach based on Semantic Depth Prediction (SDP) was proposed for small UA to perform path planning in GNSS denied environments using its onboard monocular camera. The objective of SDP isto perform 3D scene reconstruction using deep convolution neural network using 2D images captured through a single forward-looking onboard camera thus eliminating the use of expensive and complex sensors. SDP was modeled based on open-source image data set (like NYU2 and SunRGB-D) and real image data sets taken from the actual environments to improve of detection accuracy and was tested in an actual indoor warehouse to validate the performance of the proposed SDP concept. Our experiments have shown that combining lightweight mobile Convolutional neural network (CNN) models allows feature tracking navigation tasks to be undertaken by an off the shelve Tello without the need for additional sensors. However, features of interest need to be kept within the center of each frame of image to eliminate the possibility of losing feature of interest over time. Missing objects in SDP output can be linked to partially occluded objects captured in the input image as existing networks are not able to handle missing information and thus cannot detect objects under occlusion

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

Perceptual Manipulations for Hiding Image Transformations in Virtual Reality

Users of a virtual reality make frequent gaze shifts and head movements to explore their surrounding environment. Saccades are rapid, ballistic, conjugate eye movements that reposition our gaze, and in doing so create large-field motion on our retina. Due to the high speed motion on the retina during saccades, the brain suppresses the visual signals from the eye, a perceptual phenomenon known as the saccadic suppression. These moments of visual blindness can help hide the display graphical updates in a virtual reality. In this dissertation, I investigated how the visibility of various image transformations differed, during combinations of saccade and head rotation conditions. Additionally, I studied how hand and gaze interaction, affected image change discrimination in an inattentional blindness task. I conducted four psychophysical experiments in desktop or head-mounted VR. In the eye tracking studies, users viewed 3D scenes, and were triggered to make a vertical or horizontal saccade. During the saccade an instantaneous translation or rotation was applied to the virtual camera used to render the scene. Participants were required to indicate the direction of these transitions after each trial. The results showed that type and size of the image transformation affected change detectability. During horizontal or vertical saccades, rotations along the roll axis were the most detectable, while horizontal and vertical translations were least noticed. In a second similar study, I added a constant camera motion to simulate a head rotation, and in a third study, I compared active head rotation with a simulated rotation or a static head. I found less sensitivity to transsaccadic horizontal compared to vertical camera shifts during simulated or real head pan. Conversely, during simulated or real head tilt observers were less sensitive to transsaccadic vertical than horizontal camera shifts. In addition, in my multi-interactive inattentional blindness experiment, I compared sensitivity to sudden image transformations when a participant used their hand and gaze to move and watch an object, to when they only watched it move. The results confirmed that when involved in a primary task that requires focus and attention with two interaction modalities (gaze and hand), a visual stimuli can better be hidden than when only one sense (vision) is involved. Understanding the effect of continuous head movement and attention on the visibility of a sudden transsaccadic change can help optimize the visual performance of gaze-contingent displays and improve user experience. Perceptually suppressed rotations or translations can be used to introduce imperceptible changes in virtual camera pose in applications such as networked gaming, collaborative virtual reality and redirected walking. This dissertation suggests that such transformations can be more effective and more substantial during active or passive head motion. Moreover, inattentional blindness during an attention-demanding task provides additional opportunities for imperceptible updates to a visual display