279 research outputs found
A modular hybrid SLAM for the 3D mapping of large scale environments
Underground mining environments pose many unique challenges to the task of creating extensive, survey quality 3D maps. The extreme characteristics of such environments require a modular mapping solution which has no dependency on Global Positioning Systems (GPS), physical odometry, a priori information or motion model simplification. These restrictions rule out many existing 3D mapping approaches. This work examines a hybrid approach to mapping, fusing omnidirectional vision and 3D range data to produce an automatically registered, accurate and dense 3D map. A series of discrete 3D laser scans are registered through a combination of vision based bearing-only localization and scan matching with the Iterative Closest Point (ICP) algorithm. Depth information provided by the laser scans is used to correctly scale the bearing-only feature map, which in turn supplies an initial pose estimate for a registration algorithm to build the 3D map and correct localization drift. The resulting extensive maps require no external instrumentation or a priori information. Preliminary testing demonstrated the ability of the hybrid system to produce a highly accurate 3D map of an extensive indoor space
6D SLAM with Cached kd-tree Search
6D SLAM (Simultaneous Localization and Mapping) or 6D Concurrent
Localization and Mapping of mobile robots considers six degrees of
freedom for the robot pose, namely, the x, y and z coordinates
and the roll, yaw and pitch angles. In previous work we presented our
scan matching based 6D SLAM approach, where scan matching is
based on the well known iterative closest point (ICP) algorithm
[Besl 1992]. Efficient implementations of this algorithm are a
result of a fast computation of closest points. The usual approach,
i.e., using kd-trees is extended in this paper. We describe a novel
search stategy, that leads to significant speed-ups. Our mapping
system is real-time capable, i.e., 3D maps are computed using the
resources of the used Kurt3D robotic hardware
CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WITH CONTINUOUS MINER USING 3-D DEPTH CAMERA
In recent years, a great deal of work has been conducted in automating mining equipment with the goals of increasing worker health and safety and increasing mine productivity. Automating vehicles such as load-haul-dumps been successful even in underground environments where the use of global positioning systems are unavailable. This thesis addresses automating the operation of a shuttle car, specifically focusing on positioning the shuttle car under the continuous miner coal-discharge conveyor during cutting and loading operations. This task requires recognition of the target and precise control of the tramming operation because a specific orientation and distance from the coal discharge conveyor is needed to avoid coal spillage. The proposed approach uses a stereo depth camera mounted on a small-scale mockup of a shuttle car. Machine learning algorithms are applied to the camera output to identify the continuous miner coal-discharge conveyor and segment the scene into various regions such as roof, ribs, and personnel. This information is used to plan the shuttle car path to the continuous miner coal-discharge conveyor. These methods are currently applied on 1/6th scale continuous miner and shuttle car in an appropriately scaled mock mine
Mapping Complex Marine Environments with Autonomous Surface Craft
This paper presents a novel marine mapping system using an Autonomous
Surface Craft (ASC). The platform includes an extensive sensor suite for mapping
environments both above and below the water surface. A relatively small hull size
and shallow draft permits operation in cluttered and shallow environments. We address the Simultaneous Mapping and Localization (SLAM) problem for concurrent
mapping above and below the water in large scale marine environments. Our key
algorithmic contributions include: (1) methods to account for degradation of GPS
in close proximity to bridges or foliage canopies and (2) scalable systems for management of large volumes of sensor data to allow for consistent online mapping
under limited physical memory. Experimental results are presented to demonstrate
the approach for mapping selected structures along the Charles River in Boston.United States. Office of Naval Research (N00014-06-10043)United States. Office of Naval Research (N00014-05-10244)United States. Office of Naval Research (N00014-07-11102)Massachusetts Institute of Technology. Sea Grant College Program (grant 2007-R/RCM-20
Multi-agent robotic systems and exploration algorithms: Applications for data collection in construction sites
The construction industry has been notoriously slow to adopt new technology
and embrace automation. This has resulted in lower efficiency and productivity
compared to other industries where automation has been widely adopted. However,
recent advancements in robotics and artificial intelligence offer a potential
solution to this problem. In this study, a methodology is proposed to integrate
multi-robotic systems in construction projects with the aim of increasing
efficiency and productivity. The proposed approach involves the use of multiple
robot and human agents working collaboratively to complete a construction task.
The methodology was tested through a case study that involved 3D digitization
of a small, occluded space using two robots and one human agent. The results
show that integrating multi-agent robotic systems in construction can
effectively overcome challenges and complete tasks efficiently. The
implications of this study suggest that multi-agent robotic systems could
revolutionize the industry
System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment
There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps
A review of laser scanning for geological and geotechnical applications in underground mining
Laser scanning can provide timely assessments of mine sites despite adverse
challenges in the operational environment. Although there are several published
articles on laser scanning, there is a need to review them in the context of
underground mining applications. To this end, a holistic review of laser
scanning is presented including progress in 3D scanning systems, data
capture/processing techniques and primary applications in underground mines.
Laser scanning technology has advanced significantly in terms of mobility and
mapping, but there are constraints in coherent and consistent data collection
at certain mines due to feature deficiency, dynamics, and environmental
influences such as dust and water. Studies suggest that laser scanning has
matured over the years for change detection, clearance measurements and
structure mapping applications. However, there is scope for improvements in
lithology identification, surface parameter measurements, logistic tracking and
autonomous navigation. Laser scanning has the potential to provide real-time
solutions but the lack of infrastructure in underground mines for data
transfer, geodetic networking and processing capacity remain limiting factors.
Nevertheless, laser scanners are becoming an integral part of mine automation
thanks to their affordability, accuracy and mobility, which should support
their widespread usage in years to come
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