UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception

Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models is prevalent to create synthetic data to augment the learning in the ground-based imaging domain. However, severe challenges in the austere UAV-based domain require distinctive solutions to image synthesis for data augmentation. In this work, we leverage recent advancements in neural rendering to improve static and dynamic novelview UAV-based image synthesis, especially from high altitudes, capturing salient scene attributes. Finally, we demonstrate a considerable performance boost is achieved when a state-ofthe-art detection model is optimized primarily on hybrid sets of real and synthetic data instead of the real or synthetic data separately.Comment: Video Link: https://www.youtube.com/watch?v=ucPzbPLqqp

Assessment of Structure from Motion for Reconnaissance Augmentation and Bandwidth Usage Reduction

Modern militaries rely upon remote image sensors for real-time intelligence. A typical remote system consists of an unmanned aerial vehicle, or UAV, with an attached camera. A video stream is sent from the UAV, through a bandwidth-constrained satellite connection, to an intelligence processing unit. In this research, an upgrade to this method of collection is proposed. A set of synthetic images of a scene captured by a UAV in a virtual environment is sent to a pipeline of computer vision algorithms, collectively known as Structure from Motion. The output of Structure from Motion, a three-dimensional model, is then assessed in a 3D virtual world as a possible replacement for the images from which it was created. This study shows Structure from Motion results from a modifiable spiral flight path and compares the geoaccuracy of each result. A flattening of height is observed, and an automated compensation for this flattening is performed. Each reconstruction is also compressed, and the size of the compression is compared with the compressed size of the images from which it was created. A reduction of 49-60% of required space is shown

Integration of the 3D Environment for UAV Onboard Visual Object Tracking

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for traffic monitoring, video surveillance, and navigation.Comment: Accepted in MDPI Journal of Applied Science

Design Framework of UAV-Based Environment Sensing, Localization, and Imaging System

In this dissertation research, we develop a framework for designing an Unmanned Aerial Vehicle or UAV-based environment sensing, localization, and imaging system for challenging environments with no GPS signals and low visibility. The UAV system relies on the various sensors that it carries to conduct accurate sensing and localization of the objects in an environment, and further to reconstruct the 3D shapes of those objects. The system can be very useful when exploring an unknown or dangerous environment, e.g., a disaster site, which is not convenient or not accessible for humans. In addition, the system can be used for monitoring and object tracking in a large scale environment, e.g., a smart manufacturing factory, for the purposes of workplace management/safety, and maintaining optimal system performance/productivity. In our framework, the UAV system is comprised of two subsystems: a sensing and localization subsystem; and a mmWave radar-based 3D object reconstruction subsystem. The first subsystem is referred to as LIDAUS (Localization of IoT Device via Anchor UAV SLAM), which is an infrastructure-free, multi-stage SLAM (Simultaneous Localization and Mapping) system that utilizes a UAV to accurately localize and track IoT devices in a space with weak or no GPS signals. The rapidly increasing deployment of Internet of Things (IoT) around the world is changing many aspects of our society. IoT devices can be deployed in various places for different purposes, e.g., in a manufacturing site or a large warehouse, and they can be displaced over time due to human activities, or manufacturing processes. Usually in an indoor environment, the lack of GPS signals and infrastructure support makes most existing indoor localization systems not practical when localizing a large number of wireless IoT devices. In addition, safety concerns, access restriction, and simply the huge amount of IoT devices make it not practical for humans to manually localize and track IoT devices. Our LIDAUS is developed to address these problems. The UAV in our LIDAUS system conducts multi-stage 3D SLAM trips to localize devices based only on Received Signal Strength Indicator (RSSI), the most widely available measurement of the signals of almost all commodity IoT devices. Our simulations and experiments of Bluetooth IoT devices demonstrate that our system LIDAUS can achieve high localization accuracy based only on RSSIs of commodity IoT devices. Build on the first subsystem, we further develop the second subsystem for environment reconstruction and imaging via mmWave radar and deep learning. This subsystem is referred to as 3DRIMR/R2P (3D Reconstruction and Imaging via mmWave Radar/Radar to Point Cloud). It enables an exploring UAV to fly within an environment and collect mmWave radar data by scanning various objects in the environment. Taking advantage of the accurate locations given by the first subsystem, the UAV can scan an object from different viewpoints. Then based on radar data only, the UAV can reconstruct the 3D shapes of the objects in the space. mmWave radar has been shown as an effective sensing technique in low visibility, smoke, dusty, and dense fog environment. However, tapping the potential of radar sensing to reconstruct 3D object shapes remains a great challenge, due to the characteristics of radar data such as sparsity, low resolution, specularity, large noise, and multi-path induced shadow reflections and artifacts. Hence, it is challenging to reconstruct 3D object shapes based on the raw sparse and low-resolution mmWave radar signals. To address the challenges, our second subsystem utilizes deep learning models to extract features from sparse raw mmWave radar intensity data, and reconstructs 3D shapes of objects in the format of dense and detailed point cloud. We first develop a deep learning model to reconstruct a single object’s 3D shape. The model first converts mmWave radar data to depth images, and then reconstructs an object’s 3D shape in point cloud format. Our experiments demonstrate the significant performance improvement of our system over the popular existing methods such as PointNet, PointNet++ and PCN. Then we further explore the feasibility of utilizing a mmWave radar sensor installed on a UAV to reconstruct the 3D shapes of multiple objects in a space. We evaluate two different models. Model 1 is 3DRIMR/R2P model, and Model 2 is formed by adding a segmentation stage in the processing pipeline of Model 1. Our experiments demonstrate that both models are promising in solving the multiple object reconstruction problem. We also show that Model 2, despite producing denser and smoother point clouds, can lead to higher reconstruction loss or even missing objects. In addition, we find that both models are robust to the highly noisy radar data obtained by unstable Synthetic Aperture Radar (SAR) operation due to the instability or vibration of a small UAV hovering at its intended scanning point. Our research shows a promising direction of applying mmWave radar sensing in 3D object reconstruction

On the use of uavs in mining and archaeology - geo-accurate 3d reconstructions using various platforms and terrestrial views

During the last decades photogrammetric computer vision systems have been well established in scientific and commercial applications. Especially the increasing affordability of unmanned aerial vehicles (UAVs) in conjunction with automated multi-view processing pipelines have resulted in an easy way of acquiring spatial data and creating realistic and accurate 3D models. With the use of multicopter UAVs, it is possible to record highly overlapping images from almost terrestrial camera positions to oblique and nadir aerial images due to the ability to navigate slowly, hover and capture images at nearly any possible position. Multi-copter UAVs thus are bridging the gap between terrestrial and traditional aerial image acquisition and are therefore ideally suited to enable easy and safe data collection and inspection tasks in complex or hazardous environments. In this paper we present a fully automated processing pipeline for precise, metric and geo-accurate 3D reconstructions of complex geometries using various imaging platforms. Our workflow allows for georeferencing of UAV imagery based on GPS-measurements of camera stations from an on-board GPS receiver as well as tie and control point information. Ground control points (GCPs) are integrated directly in the bundle adjustment to refine the georegistration and correct for systematic distortions of the image block. We discuss our approach based on three different case studies for applications in mining and archaeology and present several accuracy related analyses investigating georegistration, camera network configuration and ground sampling distance. Our approach is furthermore suited for seamlessly matching and integrating images from different view points and cameras (aerial and terrestrial as well as inside views) into one single reconstruction. Together with aerial images from a UAV, we are able to enrich 3D models by combining terrestrial images as well inside views of an object by joint image processing to generate highly detailed, accurate and complete reconstructions