With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. In future mines, remote-controlled and autonomous haulers will operate underground guided by LiDAR sensors. We envision reusing LiDAR measurements to maintain accurate mine maps that would contribute to both safety and productivity. Extrapolating from a pilot project on reliable wireless communication in Boliden's Kankberg mine, we propose establishing a system-of-systems (SoS) with LIDAR-equipped haulers and existing mapping solutions as constituent systems. SoS requirements engineering inevitably adds a political layer, as independent actors are stakeholders both on the system and SoS levels. We present four SoS scenarios representing different business models, discussing how development and operations could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. Based on eight key variation points, we compare the four scenarios from both technical and business perspectives. Finally, we validate our findings in a seminar with participants from the relevant stakeholders. We conclude that to determine which scenario is the most promising for Boliden, trade-offs regarding control, costs, risks, and innovation must be carefully evaluated.
Huber and Vandapel and their research group also did work on underground mine mapping using robots @cite_18 . In contrast to the work by Thrun , Huber and Vandapel tried their approach in active mines. They mounted a high-resolution 3D scanner on a mobile robot, providing 8000 x 1400 pixel scans with millimeter-level accuracy. As for Groundhog, they collected scans only at certain vantage points; Each three to five meters the robot stopped for 90 seconds to obtain a complete scan. A considerable contribution of their research is related to multi-view surface matching, i.e., merging multiple 3D views into a single map. Their approach is called iterative merging, which was successfully used to create high-quality maps of an underground mine. However, their approach does not scale to large numbers of scans; Back in 2006, their approach could only generate sub-maps containing about 50 scans.
@cite_18
2888726385 2066713014 2011654214 2283534560
For several years, our research group has been developing methods for automated modeling of 3D environments. In September, 2002, we were given the opportunity to demonstrate our mapping capability in an underground coal mine. The opportunity arose as a result of the Quecreek mine accident, in which an inaccurate map caused miners to breach an abandoned, water-lled mine, trapping them for several days. Our eld test illustrates the feasibility and potential of high resolution three-dimensional (3D) mapping of an underground coal mine using a cartmounted 3D laser scanner. This paper presents our experimental setup, the automatic 3D modeling method used, and the results of the eld test. In addition, we address issues related to laser sensing in a coal mine environment. This article discusses the software architecture of an autonomous robotic system designed to explore and map abandoned mines. A new set of software tools is presented, enabling robots to acquire maps of unprecedented size and accuracy. On 30 May 2003, the robot "Groundhog" successfully explored and mapped a main corridor of the abandoned Mathies mine near Courtney, Pennsylvania. This article also discusses some of the challenges that arise in the subterranean environments and some the difficulties of building truly autonomous robots. Most current navigation algorithms in mobile robotics produce 2D maps from data provided by 2D sensors. In large part this is due to the availability of suitable 3D sensors and difficulties of managing the large amount of data supplied by 3D sensors. This paper presents a novel, multi-resolution algorithm that aligns 3D range data stored in occupied voxel lists so as to facilitate the construction of 3D maps. Multi-resolution occupied voxel lists (MROL) are voxel based data structures that efficiently represent 3D scan and map information. The process described in this research can align a sequence of scans to produce maps and localise a range sensor within a prior global map. An office environment (200 square metres) is mapped in 3D at 0.02 m resolution, resulting in a 200,000 voxel occupied voxel list. Global localisation within this map, with no prior pose estimate, is completed in 5 seconds on a 2 GHz processor. The MROL based sequential scan matching is compared to a standard iterative closest point (ICP) implementation with an error in the initial pose estimate of plus or minus 1 m and 90 degrees. MROL correctly scan matches 94 of scans to within 0.1 m as opposed to ICP with 30 within 0.1 m. We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.