Towards online mobile mapping using inhomogeneous lidar data

In this paper we present a novel approach to quickly obtain detailed 3D reconstructions of large scale environments. The method is based on the consecutive registration of 3D point clouds generated by modern lidar scanners such as the Velodyne HDL-32e or HDL-64e. The main contribution of this work is that the proposed system specifically deals with the problem of sparsity and inhomogeneity of the point clouds typically produced by these scanners. More specifically, we combine the simplicity of the traditional iterative closest point (ICP) algorithm with the analysis of the underlying surface of each point in a local neighbourhood. The algorithm was evaluated on our own collected dataset captured with accurate ground truth. The experiments demonstrate that the system is producing highly detailed 3D maps at the speed of 10 sensor frames per second

PORTABLE MULTI-CAMERA SYSTEM: FROM FAST TUNNEL MAPPING TO SEMI-AUTOMATIC SPACE DECOMPOSITION AND CROSS-SECTION EXTRACTION

The paper outlines the first steps of a research project focused on the digitalization of underground tunnels for the mining industry. The aim is to solve the problem of rapidly, semi-automatically, efficiently, and reliably digitizing complex and meandering tunnels. A handheld multi-camera photogrammetric tool is used for the survey phase, which allows for the rapid acquisition of the image dataset needed to produce the 3D data. Moreover, since often, automatic, and fast acquisitions are not supported by easy-to-use tools to access and use the data at an operational level, a second aim of the research is to define a method able to arrange and organise the gathered data so that it would be easily accessible. The proposed approach is to compute the 3D skeleton of the surveyed environment by employing tools developed for the analysis of vascular networks in medical imagery. From the computed skeletonization of the underground tunnels, a method is proposed to automatically extrapolate valuable information such as cross-sections, decomposed portions of the tunnel, and the referenced images from the photogrammetric survey. The long-term research goal is to create an effective workflow, both at the hardware and software level, that can reduce computation times, process large amounts of data, and reduce dependency on high levels of experience

CoMapping: Multi-robot Sharing and Generation of 3D-Maps applied to rural and urban scenarios

International audienceWe present an experimental study for the generation of large 3D maps using our CoMapping framework. This framework considers a collaborative approach to efficiently manage, share, and merge maps between vehicles. The main objective of this work is to perform a cooperative mapping for urban and rural environments denied of continuous-GPS service. The study is split in to 2 stages: Pre-Local and Local. In the first stage, each vehicle builds a Pre-Local map of its surroundings in real-time using laser-based measurements, then relocates the map in a global coordinate system using just the low cost GPS data from the first instant of the map construction. In the second stage, vehicles share their pre-local maps, align and merge them in a decentralized way in order to generate more consistent and larger maps, named Local maps. To evaluate performance of all the cooperative system in terms of map alignments, tests are conducted using 3 cars equipped with LiDARs and GPS receiver devices in urban outdoor scenarios of thé Ecole Centrale Nantes campus and rural environments

CoMapping: Efficient 3D-Map Sharing Methodology for Decentralized cases

International audienceCoMapping is a framework to efficient manage, share, and merge 3D map data between mobile robots. The main objective of this framework is to implement a Collaborative Mapping for outdoor environments. The framework structure is based on two stages. During the first one, the Pre-Local Mapping stage, each robot constructs a real time pre-local map of its environment using Laser Rangefinder data and low cost GPS information only in certain situations. Afterwards, the second one is the Local Mapping stage where the robots share their pre-local maps and merge them in a decentralized way in order to improve their new maps, renamed now as local maps. An experimental study for the case of decentralized cooperative 3D mapping is presented, where tests were conducted using three intelligent cars equipped with LiDAR and GPS receiver devices in urban outdoor scenarios. We also discuss the performance of the cooperative system in terms of map alignments

LOAM: Lidar Odometry and Mapping in Real-time

Abstract — We propose a real-time method for odometry and mapping using range measurements from a 2-axis lidar moving in 6-DOF. The problem is hard because the range measurements are received at different times, and errors in motion estimation can cause mis-registration of the resulting point cloud. To date, coherent 3D maps can be built by off-line batch methods, often using loop closure to correct for drift over time. Our method achieves both low-drift and low-computational complexity with-out the need for high accuracy ranging or inertial measurements. The key idea in obtaining this level of performance is the division of the complex problem of simultaneous localization and mapping, which seeks to optimize a large number of variables simultaneously, by two algorithms. One algorithm performs odometry at a high frequency but low fidelity to estimate velocity of the lidar. Another algorithm runs at a frequency of an order of magnitude lower for fine matching and registration of the point cloud. Combination of the two algorithms allows the method to map in real-time. The method has been evaluated by a large set of experiments as well as on the KITTI odometry benchmark. The results indicate that the method can achieve accuracy at the level of state of the art offline batch methods. I

3D SURVEYING & MODELING OF UNDERGROUND PASSAGES IN WWI FORTIFICATIONS

The virtual reconstruction of subterranean structures is a suitable scenario for the integration of different geomatics techniques although narrow passages, lack of light and irregular surface can arise various problems in the data acquisition as well as processing procedures. Generally the final product is a dense and detailed 3D model, whose number of triangles increases quickly according to the complexity of the object. This complexity reduces the efficient use and dissemination of the produced information therefore innovative solutions are sought. The article presents the 3D surveying and modelling of underground passages of World War I (WWI) fortifications. After the acquisition of dense point clouds by means of terrestrial scanning (TLS), a simplification and optimization workflow is performed with the aim of generating a lightweight product that keeps the maximum amount of significant information. A continuous scene representation with a 87% triangle reduction is generated, while the final precision is preserved according to a tolerance predefined by the final user. Such 3D product can be employed as basis for reconstruction, consolidation, preservation and valorisation of the WWI tunnels

Building Cultural Heritage Resilience through Remote Sensing: An Integrated Approach Using Multi-Temporal Site Monitoring, Datafication, and Web-GL Visualization

In the American West, wildfires and earthquakes are increasingly threatening the archaeological, historical, and tribal resources that define the collective identity and connection with the past for millions of Americans. The loss of said resources diminishes societal understanding of the role cultural heritage plays in shaping our present and future. This paper examines the viability of employing stationary and SLAM-based terrestrial laser scanning, close-range photogrammetry, automated surface change detection, GIS, and WebGL visualization techniques to enhance the preservation of cultural resources in California. Our datafication approach combines multi-temporal remote sensing monitoring of historic features with legacy data and collaborative visualization to document and evaluate how environmental threats affect built heritage. We tested our methodology in response to recent environmental threats from wildfire and earthquakes at Bodie, an iconic Gold Rush-era boom town located on the California and Nevada border. Our multi-scale results show that the proposed approach effectively integrates highly accurate 3D snapshots of Bodie’s historic buildings before/after disturbance, or post-restoration, with surface change detection and online collaborative visualization of 3D geospatial data to monitor and preserve important cultural resources at the site. This study concludes that the proposed workflow enhances the monitoring of at-risk California’s cultural heritage and makes a call to action to employ remote sensing as a pathway to advanced planning. View Full-Tex

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%