Novel Pole Photogrammetric System for Low-Cost Documentation of Archaeological Sites: The Case Study of “Cueva Pintada”

19 p.Close-range photogrammetry is a powerful and widely used technique for 3D reconstruction of archaeological environments, specifically when a high-level detail is required. This paper presents an innovative low-cost system that allows high quality and detailed reconstructions of indoor complex scenarios with unfavorable lighting conditions by means of close-range nadir and oblique images as an alternative to drone acquisitions for those places where the use of drones is limited or discouraged: (i) indoor scenarios in which both loss of GNSS signal and need of long exposure times occur, (ii) scenarios with risk of raising dust in suspension due to the proximity to the ground and (iii) complex scenarios with variability in the presence of nooks and vertical elements of different heights. The low-altitude aerial view reached with this system allows high-quality 3D documentation of complex scenarios helped by its ergonomic design, self-stability, lightness, and flexibility of handling. In addition, its interchangeable and remote-control support allows to board different sensors and perform both acquisitions that follow the ideal photogrammetric epipolar geometry but also acquisitions with geometry variations that favor a more complete and reliable reconstruction by avoiding occlusions. This versatile pole photogrammetry system has been successfully used to 3D reconstruct and document the “Cueva Pintada” archaeological site located in Gran Canaria (Spain), of approximately 5400 m2 with a Canon EOS 5D MARK II SLR digital camera. As final products: (i) a great quality photorealistic 3D model of 1.47 mm resolution and ±8.4 mm accuracy, (ii) detailed orthophotos of the main assets of the archaeological remains and (iii) a visor 3D with associated information on the structures, materials and plans of the site were obtained.S

Drone Obstacle Avoidance and Navigation Using Artificial Intelligence

This thesis presents an implementation and integration of a robust obstacle avoidance and navigation module with ardupilot. It explores the problems in the current solution of obstacle avoidance and tries to mitigate it with a new design. With the recent innovation in artificial intelligence, it also explores opportunities to enable and improve the functionalities of obstacle avoidance and navigation using AI techniques. Understanding different types of sensors for both navigation and obstacle avoidance is required for the implementation of the design and a study of the same is presented as a background. A research on an autonomous car is done for better understanding autonomy and learning how it is solving the problem of obstacle avoidance and navigation. The implementation part of the thesis is focused on the design of a robust obstacle avoidance module and is tested with obstacle avoidance sensors such as Garmin lidar and Realsense r200. Image segmentation is used to verify the possibility of using the convolutional neural network for better understanding the nature of obstacles. Similarly, the end to end control with a single camera input using a deep neural network is used for verifying the possibility of using AI for navigation. In the end, a robust obstacle avoidance library is developed and tested both in the simulator and real drone. Image segmentation is implemented, deployed and tested. A possibility of an end to end control is also verified by obtaining a proof of concept

From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation

Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between simulation-based evaluations of algorithms relying on simplified models, and those time-consuming vehicle tests on real-scale proving grounds. Method: This article combines findings from a systematic literature review, an in-depth analysis of results and technical concepts from contestants in a competition for self-driving miniature cars, and experiences of participating in the 2013 competition for self-driving cars. Results: A simulation-based development platform for real-scale vehicles has been adapted to support the development of a self-driving miniature car. Furthermore, a standardized platform was designed and realized to enable research and experiments in the context of future mobility solutions. Conclusion: A clear separation between algorithm conceptualization and validation in a model-based simulation environment enabled efficient and riskless experiments and validation. The design of a reusable, low-cost, and energy-efficient hardware architecture utilizing a standardized software/hardware interface enables experiments, which would otherwise require resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table

Transformer-based Flood Scene Segmentation for Developing Countries

Floods are large-scale natural disasters that often induce a massive number of deaths, extensive material damage, and economic turmoil. The effects are more extensive and longer-lasting in high-population and low-resource developing countries. Early Warning Systems (EWS) constantly assess water levels and other factors to forecast floods, to help minimize damage. Post-disaster, disaster response teams undertake a Post Disaster Needs Assessment (PDSA) to assess structural damage and determine optimal strategies to respond to highly affected neighbourhoods. However, even today in developing countries, EWS and PDSA analysis of large volumes of image and video data is largely a manual process undertaken by first responders and volunteers. We propose FloodTransformer, which to the best of our knowledge, is the first visual transformer-based model to detect and segment flooded areas from aerial images at disaster sites. We also propose a custom metric, Flood Capacity (FC) to measure the spatial extent of water coverage and quantify the segmented flooded area for EWS and PDSA analyses. We use the SWOC Flood segmentation dataset and achieve 0.93 mIoU, outperforming all other methods. We further show the robustness of this approach by validating across unseen flood images from other flood data sources.Comment: Presented at NeurIPS 2021 Workshop on Machine Learning for the Developing Worl

Carnegie Mellon Team Tartan: Mission-level Robustness with Rapidly Deployed Autonomous Aerial Vehicles in the MBZIRC 2020

For robotics systems to be used in high risk, real-world situations, they have to be quickly deployable and robust to environmental changes, under-performing hardware, and mission subtask failures. Robots are often designed to consider a single sequence of mission events, with complex algorithms lowering individual subtask failure rates under some critical constraints. Our approach is to leverage common techniques in vision and control and encode robustness into mission structure through outcome monitoring and recovery strategies, aided by a system infrastructure that allows for quick mission deployments under tight time constraints and no central communication. We also detail lessons in rapid field robotics development and testing. Systems were developed and evaluated through real-robot experiments at an outdoor test site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed International Robotics Challenge. All competition trials were completed in fully autonomous mode without RTK-GPS. Our system led to 4th place in Challenge 2 and 7th place in the Grand Challenge, and achievements like popping five balloons (Challenge 1), successfully picking and placing a block (Challenge 2), and dispensing the most water autonomously with a UAV of all teams onto an outdoor, real fire (Challenge 3).Comment: 28 pages, 26 figures. To appear in Field Robotics, Special Issues on MBZIRC 202

An augmented reality interface for multi-robot tele-operation and control

This thesis presents a seamlessly controlled human multi-robot system comprised of ground and aerial robots of semi-autonomous nature for source localization tasks. The system combines augmented reality interfaces capabilities with human supervisor\u27s ability to control multiple robots. It used advanced path planning algorithms to ensure obstacles are avoided and that the operators are free for higher-level tasks. A sensor data fused AR view is displayed which helped the users pin point source information or help the operator with the goals of the mission. The paper studies a preliminary Human Factors evaluation of this system in which several interface conditions are tested for source detection tasks

Close Formation Flight Missions Using Vision-Based Position Detection System

In this thesis, a formation flight architecture is described along with the implementation and evaluation of a state-of-the-art vision-based algorithm for solving the problem of estimating and tracking a leader vehicle within a close-formation configuration. A vision-based algorithm that uses Darknet architecture and a formation flight control law to track and follow a leader with desired clearance in forward, lateral directions are developed and implemented. The architecture is run on a flight computer that handles the process in real-time while integrating navigation sensors and a stereo camera. Numerical simulations along with indoor and outdoor actual flight tests demonstrate the capabilities of detection and tracking by providing a low cost, compact size and low weight solution for the problem of estimating the location of other cooperative or non-cooperative flying vehicles within a formation architecture