Improving point-cloud accuracy from a moving platform in field operations

Almqvist, Håkan; Magnusson, Martin; Stoyanov, Todor; Lilienthal, Achim J.

doi:10.1109/icra.2013.6630654

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Scan lines were generated assuming the Lidar is on a gimball (such that it does not tilt relative to the gravity vector). In the roadway scene, the columns along the roadway cause shadowing on the ground plane and vertical walls behind; in the off-road scene, the natural contours of the hills create terrain shadows, which are notoriously difficult to mitigate [26].…”

Section: Performance Evaluationmentioning

confidence: 99%

Mitigating Shadows in Lidar Scan Matching using Spherical Voxels

McDermott,

Rife

2022

Preprint

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In this paper we propose an approach to mitigate shadowing errors in Lidar scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the Lidar beam, it is relatively easy to eliminate shadow edges which cause systematic errors in Lidar scan matching. As we show through simulation, our proposed algorithm provides better results than ground-plane removal, the most common existing strategy for shadow mitigation. Unlike ground plane removal, our method applies to arbitrary terrains (e.g. shadows on urban walls, shadows in hilly terrain) while retaining key Lidar points on the ground that are critical for estimating changes in height, pitch, and roll. Our preprocessing algorithm can be used with a range of scan-matching methods; however, for voxel-based scan matching methods, it provides additional benefits by reducing computation costs and more evenly distributing Lidar points among voxels. 1 Matthew McDermott is a student in the Mechanical Engineering Ph.D. program at Tufts University in Medford, MA. He works in the Automated Systems and Robotics Laboratory (ASAR) with Dr. Jason Rife. He received his B.S. and M.S. degrees in Mechanical Engineering at Tufts University,

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Section: Performance Evaluationmentioning

confidence: 99%

Mitigating Shadows in Lidar Scan Matching using Spherical Voxels

McDermott,

Rife

2022

Preprint

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“…The NDT-OM combines the robustness of standard occupancy-based mapping and the compact spatial representation of NDT (Saarinen et al, 2013b), and has shown promising results when used for dynamic environments (Stoyanov et al, 2013) and Graph SLAM-based approaches for long-term operations (Einhorn and Gross, 2013). 3D-NDT has also been used to evaluate the accuracy of point cloud data collected from different sensors (Stoyanov et al, 2012b), as well as correct for distorted point clouds caused by the laser scanner undergoing appreciable movement during the acquisition of the scan (Almqvist et al, 2013). The popular Monte Carlo Localization (MCL) algorithm was reformulated for NDT-based maps and was experimentally shown to provide superior localization accuracy when compared with occupancy grid MCL for industrial environments in both static and dynamic scenarios (Saarinen et al, 2013a).…”

Section: Related Workmentioning

confidence: 99%

Scan registration using segmented region growing NDT

Das

Waslander

2014

The International Journal of Robotics Research

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The Normal Distributions Transform (NDT) scan registration algorithm divides a point cloud using rectilinear voxel cells, then models the points within each cell as a set of Gaussian distributions. A nonlinear optimization is performed in order to register the scans, however the voxel-based approach results in ill-defined cost function derivatives as points cross cell boundaries. In this work, a Segmented Region Growing NDT (SRG-NDT) variant is proposed, which first removes the ground points from the scan, then uses natural features in the environment to generate Gaussian clusters for the NDT algorithm. The removal of the ground points is shown to significantly speed up the scan registration process with negligible effect on the registration accuracy. By clustering the remaining points, the SRG-NDT approach is able to model the environment with fewer Gaussian distributions compared with the voxel-based NDT methods, which allows for a smooth and continuous cost function that guarantees that the optimization will converge. Furthermore, the use of a relatively small number of Gaussian distributions allows for a significant improvement in run-time. Experiments in both urban and forested environments demonstrate that the SRG-NDT approach is able to achieve comparable accuracy to existing methods, but with an average decrease in computation time over ICP, and NDT, of 90.1%, 95.3%, and 94.5%, respectively.

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“…This approach was utilized by Moosmann and Stiller () to develop a simultaneous localization and mapping (SLAM) algorithm tailored for a high‐frame‐rate Velodyne lidar, where the iterative closest point (ICP) (Besl & McKay, ) algorithm was employed for dense matching, and linear dewarping for motion correction. Almqvist, Magnusson, Stoyanov, and Lilienthal () allowed for more general motions by incorporating odometry.…”

Section: Literature Reviewmentioning

confidence: 99%

Pose Interpolation for Laser‐based Visual Odometry

Tong

Anderson

Dong

et al. 2014

Journal of Field Robotics

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In this paper, we present two methods for obtaining visual odometry (VO) estimates using a scanning laser rangefinder. Although common VO implementations utilize stereo camera imagery, passive cameras are dependent on ambient light. In contrast, actively illuminated sensors such as laser rangefinders work in a variety of lighting conditions, including full darkness. We leverage previous successes by applying sparse appearance‐based methods to laser intensity images, and we address the issue of motion distortion by considering the timestamps of the interest points detected in each image. To account for the unique timestamps, we introduce two estimator formulations. In the first method, we extend the conventional discrete‐time batch estimation formulation by introducing a novel frame‐to‐frame linear interpolation scheme, and in the second method, we consider the estimation problem by starting with a continuous‐time process model. This is facilitated by Gaussian process Gauss‐Newton (GPGN), an algorithm for nonparametric, continuous‐time, nonlinear, batch state estimation. Both laser‐based VO methods are compared and validated using datasets obtained by two experimental configurations. These datasets consist of 11 km of field data gathered by a high‐frame‐rate scanning lidar and a 365 m traverse using a sweeping planar laser rangefinder. Statistical analysis shows a 5.3% average translation error as a percentage of distance traveled for linear interpolation and 4.4% for GPGN in the high‐frame‐rate scenario.

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Improving point-cloud accuracy from a moving platform in field operations

Cited by 6 publications

References 20 publications

Mitigating Shadows in Lidar Scan Matching using Spherical Voxels

Mitigating Shadows in Lidar Scan Matching using Spherical Voxels

Scan registration using segmented region growing NDT

Pose Interpolation for Laser‐based Visual Odometry

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