A holistic multi-scale approach to using 3D scanning technology in accident reconstruction

Three-dimensional scanning and documentation methods are becoming increasingly employed by law enforcement personnel for crime scene and accident scene recording. Three-dimensional documentation of the victim’s body in such cases is also increasingly used as the field of forensic radiology and imaging is expanding rapidly. These scanning technologies enable a more complete and detailed documentation than standard autopsy. This was used to examine a fatal pedestrian-vehicle collision where the pedestrian was killed by a van whilst crossing the road. Two competing scenarios were considered for the vehicle speed calculation: the pedestrian being projected forward by the impact or the pedestrian being carried on the vehicle’s bonnet. In order to assist with this, the impact area of the accident vehicle was scanned using laser surface scanning, the victim was scanned using postmortem CT and micro-CT and the data sets were combined to virtually match features of the vehicle to injuries on the victim. Micro-CT revealed additional injuries not previously detected, lending support to the pedestrian-carry theory

Active SLAM for autonomous underwater exploration

Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps

This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the quality of deep neural network predictions. In a supervised learning scenario, the quality of predictions is intrinsically related to the training labels, which guide the optimization process. For indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to provide dense, albeit short-range, depth maps. On the other hand, for outdoor scenes, LiDARs are considered the standard sensor, which comparatively provides much sparser measurements, especially in areas further away. Rather than modifying the neural network architecture to deal with sparse depth maps, this article introduces a novel densification method for depth maps, using the Hilbert Maps framework. A continuous occupancy map is produced based on 3D points from LiDAR scans, and the resulting reconstructed surface is projected into a 2D depth map with arbitrary resolution. Experiments conducted with various subsets of the KITTI dataset show a significant improvement produced by the proposed Sparse-to-Continuous technique, without the introduction of extra information into the training stage.Comment: Accepted. (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Documenting Bronze Age Akrotiri on Thera using laser scanning, image-based modelling and geophysical prospection

The excavated architecture of the exceptional prehistoric site of Akrotiri on the Greek island of Thera/Santorini is endangered by gradual decay, damage due to accidents, and seismic shocks, being located on an active volcano in an earthquake-prone area. Therefore, in 2013 and 2014 a digital documentation project has been conducted with support of the National Geographic Society in order to generate a detailed digital model of Akrotiri’s architecture using terrestrial laser scanning and image-based modeling. Additionally, non-invasive geophysical prospection has been tested in order to investigate its potential to explore and map yet buried archaeological remains. This article describes the project and the generated results

From pixel to mesh: accurate and straightforward 3D documentation of cultural heritage from the Cres/Lošinj archipelago

Most people like 3D visualizations. Whether it is in movies, holograms or games, 3D (literally) adds an extra dimension to conventional pictures. However, 3D data and their visualizations can also have scientic archaeological benets: they are crucial in removing relief distortions from photographs, facilitate the interpretation of an object or just support the aspiration to document archaeology as exhaustively as possible. Since archaeology is essentially a spatial discipline, the recording of the spatial data component is in most cases of the utmost importance to perform scientic archaeological research. For complex sites and precious artefacts, this can be a di€cult, time-consuming and very expensive operation. In this contribution, it is shown how a straightforward and cost-eective hard- and software combination is used to accurately document and inventory some of the cultural heritage of the Cres/Lošinj archipelago in three or four dimensions. First, standard photographs are acquired from the site or object under study. Secondly, the resulting image collection is processed with some recent advances in computer technology and so-called Structure from Motion (SfM) algorithms, which are known for their ability to reconstruct a sparse point cloud of scenes that were imaged by a series of overlapping photographs. When complemented by multi-view stereo matching algorithms, detailed 3D models can be built from such photo collections in a fully automated way. Moreover, the software packages implementing these tools are available for free or at very low-cost. Using a mixture of archaeological case studies, it will be shown that those computer vision applications produce excellent results from archaeological imagery with little eort needed. Besides serving the purpose of a pleasing 3D visualization for virtual display or publications, the 3D output additionally allows to extract accurate metric information about the archaeology under study (from single artefacts to entire landscapes)