Fast calibration of rotating and swivelling 3-D laser scanners exploiting measurement redundancies

Oberländer, Jan; Pfotzer, L.; Roennau, Arne; Dillmann, Rüdiger

doi:10.1109/iros.2015.7353796

Cited by 12 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Last but not least we are preparing a benchmark to retrieve comparable results with some of the calibration procedures discussed in section II. Even though we are still working on the benchmark our first results indicate a correlation between the number of measurements taken into account by a calibration method and the precision of the results: Traditional methods relying on only a few measurement points on a specific calibration target are very prone to sensor noise and get clearly outperformed by bundle adjustment approaches like [5], [8] and ours.…”

Section: Discussionmentioning

confidence: 95%

“…Our algorithm has been tested on three synthetic datasets and one real dataset recorded in our laboratory. The simulated environments used for the synthetic datasets contain cubic rooms with edge lengths of 5 m, 10 m and 20 m, similar to the ones used in [8]. The robot is mounted at a position of 25 % of the edge length in x and 33 % in y direction on a pillar 90 cm above the ground.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, due to this limited motion pattern of the sensors they were only able to estimate 4 DOF: As the sensors only move in a plane it is impossible to calculate their position along the rotation axis as well as the offset from the rotation axis' zero angle. [8] applied this approach to a laser following a swiveling motion pattern [9]. On top they show that working with a down-scaled copy of the original dataset gives the same results as working with the full scan but requires less computing time.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP

Peters

Schmidt

Knoll

2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

We propose a calibration method for the six degrees of freedom (DOF) extrinsic pose of a 2D laser rangefinder mounted to a robot arm. Our goal is to design a system that allows on-site re-calibration without requiring any kind of special environment or calibration objects. By moving the sensor we generate 3D scans of the surrounding area on which we run a iterative closest point (ICP) variant to estimate the missing part of the kinematic chain. With this setup we can simply scale the density and format of our 3D scan by adjusting the robot speed and trajectory, allowing us to exploit the power of a high resolution 3D scanner for a variety of tasks such as mapping, object recognition and grasp planning. Our evaluation, performed on synthetic datasets as well as from real-data shows that the presented approach provides good results both in terms of convergence on crude initial parameters as well as in the precision of the final estimate.

show abstract

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP

Peters

Schmidt

Knoll

2020

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

show abstract

“…In [16], Oberländer et al utilize the crispness introduced by Sheehan [10], and also the redundancy in the observation of a rotating or swivelling 2D laser range finder, mounted on their sensor platform, after a full rotation of the LRF. Given the case that the surrounding environment is static, the scanner has seen everything twice after a full revolution.…”

Section: Targetless Calibrationmentioning

confidence: 99%

“…A high crispness, depending on the 6-DOF pose parameters of the scanner encodes a low deviation of points in their respective neighbourhood, which, in return, means, that point pairs in the redundant data are close together, or, in other words, represent the same surface patch. In both methods, Sheehan's [10] and Oberländer's [16], the crispness is optimised using iterative, numerical Newton-like algorithms. However, ambiguities emerge between translation and rotation when the sensors are rotated eccentrically on the platform, i.e., rotated around the center with a radius r > 0.…”

Section: Targetless Calibrationmentioning

confidence: 99%

Calibration of a Rotating or Revolving Platform with a LiDAR Sensor

2019

View full text Add to dashboard Cite

Perceiving its environment in 3D is an important ability for a modern robot. Today, this is often done using LiDARs which come with a strongly limited field of view (FOV), however. To extend their FOV, the sensors are mounted on driving vehicles in several different ways. This allows 3D perception even with 2D LiDARs if a corresponding localization system or technique is available. Another popular way to gain most information of the scanners is to mount them on a rotating carrier platform. In this way, their measurements in different directions can be collected and transformed into a common frame, in order to achieve a nearly full spherical perception. However, this is only possible if the kinetic chains of the platforms are known exactly, that is, if the LiDAR pose w.r.t. to its rotation center is well known. The manual measurement of these chains is often very cumbersome or sometimes even impossible to do with the necessary precision. Our paper proposes a method to calibrate the extrinsic LiDAR parameters by decoupling the rotation from the full six degrees of freedom transform and optimizing both separately. Thus, one error measure for the orientation and one for the translation with known orientation are minimized subsequently with a combination of a consecutive grid search and a gradient descent. Both error measures are inferred from spherical calibration targets. Our experiments with the method suggest that the main influences on the calibration results come from the the distance to the calibration targets, the accuracy of their center point estimation and the search grid resolution. However, our proposed calibration method improves the extrinsic parameters even with unfavourable configurations and from inaccurate initial pose guesses.

show abstract

Robot self‐calibration using actuated 3D sensors

Peters,

Knoll

2023

Journal of Field Robotics

View full text Add to dashboard Cite

Both robot and hand‐eye calibration have been object of research for decades. While current approaches manage to precisely and robustly identify the parameters of a robot's kinematic model, they still rely on external devices such as calibration objects, markers and/or external sensors. Instead of trying to fit recorded measurements to a model of a known object, this paper treats robot calibration as an offline SLAM problem, where scanning poses are linked to a fixed point in space via a moving kinematic chain. As such, we enable robot calibration by using nothing but an arbitrary eye‐in‐hand depth sensor. To the authors' best knowledge the presented framework is the first solution to three‐dimensional (3D) sensor‐based robot calibration that does not require external sensors nor reference objects. Our novel approach utilizes a modified version of the Iterative Corresponding Point algorithm to run bundle adjustment on multiple 3D recordings estimating the optimal parameters of the kinematic model. A detailed evaluation of the system is shown on a real robot with various attached 3D sensors. The presented results show that the system reaches precision comparable to a dedicated external tracking system at a fraction of its cost.

show abstract

Fast calibration of rotating and swivelling 3-D laser scanners exploiting measurement redundancies

Cited by 12 publications

References 12 publications

Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP

Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP

Calibration of a Rotating or Revolving Platform with a LiDAR Sensor

Robot self‐calibration using actuated 3D sensors

Contact Info

Product

Resources

About