FLIRT - Interest regions for 2D range data

Tipaldi, Gian Diego; Arras, Kai O.

doi:10.1109/robot.2010.5509864

Cited by 101 publications

(91 citation statements)

References 15 publications

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“…To train, FLIRT [18] features and 128-dimensional descriptors are extracted for every incoming scan. The generated descriptors are inserted into randomized kd-trees provided by the FLANN library [20], granting the ability to rapidly query for nearest neighbors in the 128-dimensional feature space.…”

Section: A Correspondence Detectionmentioning

confidence: 99%

“…However, this strategy is not view-point invariant which makes it unsuitable for a multi-robot localization task. Several works have developed laser-based feature detection and description methods [17], [18] that can be used to rapidly detect correspondences. We choose to use FLIRT features [18] introduced by Tipaldi et al due to their rotational invariance.…”

Section: Introductionmentioning

confidence: 99%

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Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

Dong

Nelson

Indelman

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.

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Section: A Correspondence Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

Dong

Nelson

Indelman

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…So the digital map and the localization that uses it should be based on natural landmarks. Promising results are shown with FLIRT features [8] and occupancy grid maps [9]. However, there exists also the graph-based SLAM solutions based directly on point clouds.…”

Section: Future Workmentioning

confidence: 99%

Strategies for 3D data acquisition and mapping in large-scale modern warehouses

Beinschob

Reinke

2013

2013 IEEE 9th International Conference on Intelligent Computer Communication and Processing (ICCP)

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Today modern manufacturing and logistic processes rely in most instances on manual forklifts. Being known for low efficiency, high energy consumption and listed among the most frequent causes of severe accidents in factories, the SICK AG coordinated EU funded research project PAN Robots aims to replace them in large scale with Automated Guided Vehicles (AGV). Major obstacles for operators with current AGVs are high initial costs, e.g. registration of all pallet positions and reflectors which needs to be performed by highly trained professionals in order to create an accurate navigation map for the AGVs. Simultaneous Localization And Mapping (SLAM) is the scientific approach to initial mapping. In this paper, we present 3D mapping strategies dedicated for the warehouse environment with multiple Laserscanners. Preliminary results are shown based on measurement data captured at the site of our project partner Casbega, Madrid, Spain.

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“…We evaluated several laser-based methods for pose estimation such as exhaustive search [5] and feature-based approaches [6,7]. However, because our limited onboard computational resources, we chose the Iterative Closest Point (lCP) algo rithm [8], which yields a robust and inexpensive continuous pose estimate.…”

Section: A Pose Estimationmentioning

confidence: 99%

Autonomous multi-floor indoor navigation with a computationally constrained micro aerial vehicle

Shen

Michael

Kumar

2011

2011 IEEE International Conference on Robotics and Automation

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We are interested in the problem of surveilling and ex ploring environments that include both indoor and outdoor settings. Aerial vehicles offer mobility and perspective ad vantages over ground platforms and micro aerial vehicles (MAYs) are particularly applicable to buildings with multiple floors where stairwells can be an obstacle to ground vehicles. A challenge when operating in indoor environments is the lack of an external source of localization such as GPS. For these reasons, in this work we focus on autonomous navigation in buildings with multiple floors without requiring an external source of localization or prior knowledge of the environment. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands. Therefore, we pur sue a system design and methodology capable of autonomous navigation with real-time performance on a mobile processor using only onboard sensors (Fig. 1); where in this work autonomous navigation considers multi-floor mapping with loop closure, localization, planning, and control.We note that the topic of autonomous navigation with a MAY is addressed by others in the community with some similarities in approach and methodology. Relevant to this paper is the work of Bachrach et al. [1,2], Grzonka et al. [3], and Blosch et al. [4] with results toward online autonomous navigation and exploration with an aerial vehicle. The major points of differentiation between existing results and our work are threefold. First, all the processing is done onboard requiring algorithms that lend themselves to real-time com putation on a small processor. Second, we consider multi floor operation with loop closure. Third, we design adaptive controllers to compensate for external aerodynamic effects which would otherwise prohibit operation in constrained environments. II. MET HOD OL OGY AND RELATED LITERATUREThe discussion follows the logical flow of the system design (Fig. 2). The six degree-of-freedom (DOF) pose of the robot is defined by its 3D position, roll, pitch, and yaw Euler angles, {x, y, z, ¢;, e, "p}. S. Shen, N. Michael, and V. Kumar are with the GRASP Laboratory, Fig. I. The experimental platform with onboard computation (1.6 GHz Atom processor) and sensing (laser, camera, and IMU). A. Pose EstimationA scanning laser range sensor retrofitted with mirrors for beam redirection to the floor and ceiling serves as a primary source of information for position and yaw estimation. We evaluated several laser-based methods for pose estimation such as exhaustive search [5] and feature-based approaches [6,7]. However, because our limited onboard computational resources, we chose the Iterative Closest Point (lCP) algo rithm [8], which yields a robust and inexpensive continuous pose estimate. We make use of a grid based search [9] to speed up the computationally expensive closest point search in ICP. The result of the ICP algorithm is an estimate of {x, y, "p}. The algorit...

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FLIRT - Interest regions for 2D range data

Cited by 101 publications

References 15 publications

Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

Strategies for 3D data acquisition and mapping in large-scale modern warehouses

Autonomous multi-floor indoor navigation with a computationally constrained micro aerial vehicle

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