Continuous 3D scan-matching with a spinning 2D laser

Bosse, Michael; Zlot, Robert

doi:10.1109/robot.2009.5152851

Cited by 263 publications

(252 citation statements)

References 10 publications

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“…The estimate of the 3D pose difference between the two points in time is then used to optimize the robot trajectory in between. In a similar approach Bosse and Zlot (2009) use a modified ICP with a custom correspondence search to optimize the pose of six discrete points in time of the trajectory of a robot during a single scanner rotation.…”

Section: Calibration Referencing and Slammentioning

confidence: 99%

“…The algorithm is adopted from Elseberg et al (2013), where it was used in a different mobile mapping context, i.e., on wheeled platforms. Unlike other state of the art algorithms, like (Stoyanov and Lilienthal, 2009) and (Bosse and Zlot, 2009), it is not restricted to purely local improvements. We make no rigidity assumptions, except for the computation of the point correspondences.…”

Section: Continuous-time Slammentioning

confidence: 99%

See 1 more Smart Citation

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

Nüchter

Bleier

Schauer

et al. 2017

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

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ABSTRACT:This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google's results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.

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Section: Calibration Referencing and Slammentioning

confidence: 99%

Section: Continuous-time Slammentioning

confidence: 99%

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

Nüchter

Bleier

Schauer

et al. 2017

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

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show abstract

“…We overcome this sparsity through probabilistic assignments of surfels during the registration process. While the many methods assume the robot to stand still during 3D scan acquisition, some approaches also integrate scan lines of a continuously rotating laser scanner into 3D maps while the robot is moving [4,7,24,13,1]. Up to now, such 3D laser scanners are rarely employed on lightweight MAVs due to their payload limitations and the difficulty of aggregating 3D scans in-flight.…”

Section: Related Workmentioning

confidence: 99%

Local Multi-resolution Surfel Grids for MAV Motion Estimation and 3D Mapping

Droeschel

Stückler

Behnke

2015

Intelligent Autonomous Systems 13

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Abstract. For autonomous navigation in restricted environments, micro aerial vehicles (MAV) need to create 3D maps of their surroundings and must track their motion within these maps. In this paper, we propose an approach to simultaneous localization and mapping that is based on the measurements of a lightweight 3D laser-range finder. We aggregate laser-range measurements by registering sparse 3D scans with a local multiresolution surfel map that has high resolution in the vicinity of the MAV and coarser resolutions with increasing distance, which corresponds well to measurement density and accuracy of our sensor. Modeling measurement distributions within voxels by surface elements allows for efficient and accurate registration of 3D scans with the local map. The incrementally built local dense 3D maps of nearby key poses are registered globally by graph optimization. This yields a globally consistent dense 3D map of the environment. Continuous registration of local maps with the global map allows for tracking the 6D MAV pose in real time.In experiments, we demonstrate accuracy and efficiency of our approach.

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“…The system is handheld, extremely lightweight and uses a nodding scanner motion to acquire 3D data. Previously, Bosse and Zlot (2009) considered also spinning SICK laser scanners. Their point cloud optimization algorithm considers planar patches extracted from a sweep and deforms the trajectroy using a spline.…”

Section: Semi-rigid Slam For Trajectory Optimizationmentioning

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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Continuous 3D scan-matching with a spinning 2D laser

Cited by 263 publications

References 10 publications

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

Local Multi-resolution Surfel Grids for MAV Motion Estimation and 3D Mapping

A Study of Scan Patterns for Mobile Mapping

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