Generation and Rendering of Interactive Ground Vegetation for Real-Time Testing and Validation of Computer Vision Algorithms

During the development process of new algorithms for computer vision applications, testing and evaluation in real outdoor environments is time-consuming and often difficult to realize. Thus, the use of artificial testing environments is a flexible and cost-efficient alternative. As a result, the development of new techniques for simulating natural, dynamic environments is essential for real-time virtual reality applications, which are commonly known as Virtual Testbeds. Since the first basic usage of Virtual Testbeds several years ago, the image quality of virtual environments has almost reached a level close to photorealism even in real-time due to new rendering approaches and increasing processing power of current graphics hardware. Because of that, Virtual Testbeds can recently be applied in application areas like computer vision, that strongly rely on realistic scene representations. The realistic rendering of natural outdoor scenes has become increasingly important in many application areas, but computer simulated scenes often differ considerably from real-world environments, especially regarding interactive ground vegetation. In this article, we introduce a novel ground vegetation rendering approach, that is capable of generating large scenes with realistic appearance and excellent performance. Our approach features wind animation, as well as object-to-grass interaction and delivers realistically appearing grass and shrubs at all distances and from all viewing angles. This greatly improves immersion, as well as acceptance, especially in virtual training applications. Nevertheless, the rendered results also fulfill important requirements for the computer vision aspect, like plausible geometry representation of the vegetation, as well as its consistence during the entire simulation. Feature detection and matching algorithms are applied to our approach in localization scenarios of mobile robots in natural outdoor environments. We will show how the quality of computer vision algorithms is influenced by highly detailed, dynamic environments, like observed in unstructured, real-world outdoor scenes with wind and object-to-vegetation interaction

Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info: https://ori.ox.ac.uk/esm-localizatio

Visual 3-D SLAM from UAVs

The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs

Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation

Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest. Forests are unstructured environments where it is difficult to find a group of significant landmarks for current fast feature-based place recognition algorithms. This paper proposes a novel approach where local observations are matched to a general tree map using the Delaunay triangularization as the representation format. Instead of point cloud based matching methods, we utilize a topology-based method. First, tree trunk positions are registered at a prior run done by a forest harvester. Second, the resulting map is Delaunay triangularized. Third, a local submap of the autonomous robot is registered, triangularized and matched using triangular similarity maximization to estimate the position of the robot. We test our method on a dataset accumulated from a forestry site at Lieksa, Finland. A total length of 2100\,m of harvester path was recorded by an industrial harvester with a 3D laser scanner and a geolocation unit fixed to the frame. Our experiments show a 12\,cm s.t.d. in the location accuracy and with real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and speed limit is realistic during forest operations

Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition