Rough Terrain 3D Mapping and Navigation Using a Continuously Rotating 2D Laser Scanner

Schadler, Mark; Stückler, Jörg; Behnke, Sven

doi:10.1007/s13218-014-0301-8

Cited by 22 publications

(10 citation statements)

References 22 publications

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“…In past years, 3D data acquisition from UGVs was usually performed in a stop-and-go manner [10,11], but, nowadays, it can be performed in motion by using some kind of simultaneous localization and mapping (SLAM) by combining data from odometry [12] or from an inertial unit [13].…”

Section: Introductionmentioning

confidence: 99%

Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

et al. 2018

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Abstract:The paper describes the case study of the mobile robot Andabata navigating on natural terrain at low speeds with fuzzy elevation maps (FEMs). To this end, leveled three-dimensional (3D) point clouds of the surroundings are obtained by synchronizing ranges obtained from a 360 • field of view 3D laser scanner with odometric and inertial measurements of the vehicle. Then, filtered point clouds are employed to produce FEMs and their corresponding fuzzy reliability masks (FRMs). Finally, each local FEM and its FRM are processed to choose the best motion direction to reach distant goal points through traversable areas. All these tasks have been implemented using ROS (Robot Operating System) nodes distributed among the cores of the onboard processor. Experimental results of Andabata during non-stop navigation on an urban park are presented.

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Section: Introductionmentioning

confidence: 99%

Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

et al. 2018

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“…Minimizing the number of viewpoints using Art Gallery Problem (AGP) is another approach to compute the optimal path [27,28,29]. Coverage path planning has been mostly applied on 3D-mapping [30,31], surveying of urban environments [32,33,34], ship hull inspection [28,35,36], and structural inspection [37]. The model of Bircher et al [37] which is based on Lin-Kernighan Heuristic (LKH) as a TSP solver, convex optimization, and Rapidly-exploring Random Tree star (RRT*) algorithm to define the optimized viewpoints and path, can be applied for other types of structures.…”

Section: Uav Path Planningmentioning

confidence: 99%

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

Bolourian¹,

Soltani²,

Hammad³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-According to Statistics Canada, bridges in Canada have a service life of approximately 43 years. With the majority of bridges passing half of their expected service life, a large amount of investment needs to be made to inspect and maintain them in a safe condition. Manual inspection methods are both time-consuming and costly, which may discourage further inspections and follow-up of defects. Thus, there is a great need for using an automated data collection system. Surface defects (e.g. cracks) in concrete bridges can be inspected using 3D Light Detection and Ranging (LiDAR) scanner as a Non-Destructive Testing (NDT) method. However, the commonly used terrestrial LiDAR is limited to stationary data collection, which reduces the accessibility to some components of the bridges. To tackle this limitation, a LiDAR attached to an Unmanned Aerial Vehicle (UAV) provides more flexibility and accessibility for inspecting large surface areas without threatening inspectors' safety. After providing a comprehensive literature review about the usage of UAVs and LiDAR for the inspection of different types of structures, this paper proposes a high level framework for bridge inspection using LiDAR-equipped UAV. The framework includes the following steps: (1) planning a collisionfree optimized path with respect to the minimum cost and maximum coverage considering a variety of constrains and requirements related to the hardware and the inspection task, and (2) data analysis for detecting the surface defects based on the collected point clouds.

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“…The following sections will describe the navigation stack in more detail. Full descriptions of the allocentric and egocentric navigation components can be found in Schadler, Stückler, & Behnke () and Schwarz & Behnke (), respectively.…”

Section: Rough Terrain Navigationmentioning

confidence: 99%

NimbRo Explorer: Semiautonomous Exploration and Mobile Manipulation in Rough Terrain

Stückler

Schwarz

Schadler

et al. 2015

Journal of Field Robotics

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Fully autonomous exploration and mobile manipulation in rough terrain are still beyond the state of the art—robotics challenges and competitions are held to facilitate and benchmark research in this direction. One example is the 2013 DLR SpaceBot Cup, for which we developed an integrated robot system to semiautonomously perform planetary exploration and manipulation tasks. Our robot explores, maps, and navigates in previously unknown, uneven terrain using a three‐dimensional laser scanner and an omnidirectional RGB‐D camera. We developed manipulation capabilities for object retrieval and pick‐and‐place tasks. Many parts of the mission can be performed autonomously. In addition, we developed teleoperation interfaces on different levels of shared autonomy, which allow for specifying missions, monitoring mission progress, and on‐the‐fly reconfiguration. To handle network communication interruptions and latencies between robot and operator station, we implemented a robust network layer for the middleware Robot Operating System (ROS). The integrated system has been demonstrated at the 2013 DLR SpaceBot Cup. In addition, we conducted systematic experiments to evaluate the performance of our approaches.

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Rough Terrain 3D Mapping and Navigation Using a Continuously Rotating 2D Laser Scanner

Cited by 22 publications

References 22 publications

Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

Field Navigation Using Fuzzy Elevation Maps Built with Local 3D Laser Scans

High Level Framework for Bridge Inspection Using LiDAR-Equipped UAV

NimbRo Explorer: Semiautonomous Exploration and Mobile Manipulation in Rough Terrain

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