6D SLAM with an application in autonomous mine mapping

Nüchter, Andreas; Surmann, Hartmut; Lingemann, Kai; Hertzberg, Joachim; Thrun, Sebastian

doi:10.1109/robot.2004.1308117

Cited by 203 publications

(130 citation statements)

References 15 publications

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“…The method is not restricted to polygonal environments, independent from structural alteration of the surrounding like beacons or artificial landmarks, and proved to be stable even in dynamic environments. The presented method succeeded in providing our 6D-SLAM algorithm [1,2] with a required pose estimation and enables the system to build three-dimensional indoor and outdoor maps.…”

Section: Resultsmentioning

confidence: 99%

“…In discrete distances, the robot stops and acquires a 3D scan (duration: 6.8 s for 361 × 256 data points). These 3D scans are registered into one consistent model with the 6D-SLAM algorithm described in [1,2]. 3 …”

Section: Outdoor Trackingmentioning

confidence: 99%

“…The aim of relative pose tracking is not directly to build an accurate 2D map, but rather to ensure a stable and fast localization, regardless of the robot's speed and without any restrictions of the covered distance. This aim reflects the underlying idea that localization, the preliminary stage of SLAM, is not an end in itself, but the fundament of further algorithms, e.g., the building of three-dimensional environmental maps with a 3D sensor and solving 6D-SLAM [1,2]. The localization algorithm has to be computationally efficient and should not restrict the operational area or the speed of the mobile robot.…”

Section: Introductionmentioning

confidence: 98%

“…In earlier publications we proposed methods for solving 6D-SLAM (simultaneous localization and mapping) based on 3D laser range data [1,2]. These algorithms depend on a sufficiently correct pose estimation of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

“…But wheels slip, especially when driving with high speed, and miscalculation occurs. 1 These errors accumulate over time, and the robot's position estimate becomes increasingly inaccurate. One method to improve localization is to track the pose with an on-board laser range finder.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

High-speed laser localization for mobile robots

Lingemann

Nüchter

Hertzberg

et al. 2005

Robotics and Autonomous Systems

145

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This paper describes a novel, laser-based approach for tracking the pose of a high-speed mobile robot. The algorithm is outstanding in terms of accuracy and computation time. The efficiency is achieved by a closed-form solution for the matching of two laser scans, the use of natural scan features and fast linear filters. The implemented algorithm is evaluated with the high-speed robot Kurt3D (4 m/s), and compared to standard scan matching methods in indoor and outdoor environments.

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Section: Resultsmentioning

confidence: 99%

Section: Outdoor Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

High-speed laser localization for mobile robots

Lingemann

Nüchter

Hertzberg

et al. 2005

Robotics and Autonomous Systems

145

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3D Scan matching for mobile robot localization over rough terrain

Nakagomi¹,

Fuse²,

Hosaka³

et al. 2019

Electrical Engineering Japan

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In order to enable an autonomous mobile robot to travel over rough terrain, it necessitates the capability to detect self-position accurately even when the odometry errors are increased in traveling. The conventional method can keep high speed and precise localization using iterative closest point algorithms or feature matching techniques.However, effects of steep changes of a mobile robot position are not considered when it travels over rough terrain. In this article, we propose the method for efficient realtime 6D pose tracking using a rotating 2D laser scanner in traveling over rough terrain.For adaptation to steep changes of the position, weighted point clouds are generated based on the angular and the linear velocity measured by sensors mounted on the robot. And the position and posture of the robot are sequentially optimized by the scan matching in increments of 10 scans. In indoor experiments, we evaluated accuracy of our method when the robot passes on rugged floor. As a result, our method was performed with less than 0.078 m RMS positional error in real time. K E Y W O R D Slaser range finder, odometry, scan matching 14

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A Self‐Consistent Bathymetric Mapping Algorithm

Roman

Singh

2007

Journal of Field Robotics

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The achievable accuracy of bathymetric mapping in the deep ocean using robotic systems is most often limited by the available guidance or navigation information used to combine the measured sonar ranges during the map making process. This paper presents an algorithm designed to mitigate the affects of poor ground referenced navigation by applying the principles of map registration and pose filtering commonly used in simultaneous localization and mapping ͑SLAM͒ algorithms. The goal of the algorithm is to produce a self-consistent point cloud representation of the bottom terrain with errors that are on a scale similar to the sonar range resolution rather than any direct positioning measurement. The presented algorithm operates causally and utilizes sensor data that are common to instrumented underwater robotic vehicles used for mapping and scientific explorations. Real world results are shown for data taken on several expeditions with the JASON remotely operated vehicle ͑ROV͒. Comparisons are made between more standard mapping approaches and the proposed method is shown to significantly improve the map quality and reveal scene information that would have otherwise been obscured due to poor direct navigation information.

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6D SLAM with an application in autonomous mine mapping

Cited by 203 publications

References 15 publications

High-speed laser localization for mobile robots

High-speed laser localization for mobile robots

3D Scan matching for mobile robot localization over rough terrain

A Self‐Consistent Bathymetric Mapping Algorithm

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