Globally consistent 3D mapping with scan matching

Borrmann, Dorit; Elseberg, Jan; Lingemann, Kai; Nüchter, Andreas; Hertzberg, Joachim

doi:10.1016/j.robot.2007.07.002

Cited by 255 publications

(188 citation statements)

References 31 publications

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“…Another important feature of our method is that it works with raw 3D data, it is said, unorganized 3D points sets. In contrast to other previous works on 6D mapping like Borrmann et al (2008); Kümmerle et al (2008); Censi (2008); Armesto et al (2010) this feature makes our method independent of the 3D sensor used to obtain the data. In this way, applying our method for different robots equipped with different sensors does not require a big programming effort.…”

Section: Resultsmentioning

confidence: 94%

A robust and fast method for 6DoF motion estimation from generalized 3D data

Viejo

Cazorla

2013

Auton Robot

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Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six Degrees of Freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.

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Section: Resultsmentioning

confidence: 94%

A robust and fast method for 6DoF motion estimation from generalized 3D data

Viejo

Cazorla

2013

Auton Robot

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“…To understand the basic idea of the used continuous-time SLAM, we summarize its foundation, 6DSLAM, which was designed for a high-precision registration of terrestrial 3D scans, i.e., globally consistent scan matching (Borrmann et al, 2008). It is available in 3DTK -The 3D Toolkit.…”

Section: Continuous-time Slam Algorithm 6d Slammentioning

confidence: 99%

Evaluating Continuous-Time Slam Using a Predefined Trajectory Provided by a Robotic Arm

Koch¹,

Leblebici²,

Martell³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Recently published approaches to SLAM algorithms process laser sensor measurements and output a map as a point cloud of the environment. Often the actual precision of the map remains unclear, since SLAM algorithms apply local improvements to the resulting map. Unfortunately, it is not trivial to compare the performance of SLAM algorithms objectively, especially without an accurate ground truth. This paper presents a novel benchmarking technique that allows to compare a precise map generated with an accurate ground truth trajectory to a map with a manipulated trajectory which was distorted by different forms of noise. The accurate ground truth is acquired by mounting a laser scanner on an industrial robotic arm. The robotic arm is moved on a predefined path while the position and orientation of the end-effector tool are monitored. During this process the 2D profile measurements of the laser scanner are recorded in six degrees of freedom and afterwards used to generate a precise point cloud of the test environment. For benchmarking, an offline continuous-time SLAM algorithm is subsequently applied to remove the inserted distortions. Finally, it is shown that the manipulated point cloud is reversible to its previous state and is slightly improved compared to the original version, since small errors that came into account by imprecise assumptions, sensor noise and calibration errors are removed as well.

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“…The following subsections summarize our work in Borrmann et al (2008); Elseberg et al, 2013 (submitted). These algorithms are suited to turn laser range data acquired with a rotating scanner while the acquisition system is in motion into globally consistent 3D point clouds.…”

Section: Mobile Mapping With Constantly Spinning Scannersmentioning

confidence: 99%

A Study of Scan Patterns for Mobile Mapping

Elsberg

Borrmann

Nüchter

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mobile terrestrial scanning systems automate terrestrial laser scanning. Continous scanning mobile terrestrial systems constantly spin the terrestrial laser scanner and thus combine terrestrial scanning with kinematic laser scanning. This paper presents a scan pattern analysis for these systems. We aim at finding the most advantageous combination of terrestrial and kinematic systems. The resulting 3D point cloud depends on the scan pattern and the trajectory and velocity of the mobile system.

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Globally consistent 3D mapping with scan matching

Cited by 255 publications

References 31 publications

A robust and fast method for 6DoF motion estimation from generalized 3D data

A robust and fast method for 6DoF motion estimation from generalized 3D data

Evaluating Continuous-Time Slam Using a Predefined Trajectory Provided by a Robotic Arm

A Study of Scan Patterns for Mobile Mapping

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