Point-to-Surfel-Distance- (PSD-) Based 6D Localization Algorithm for Rough Terrain Exploration Using Laser Scanner in GPS-Denied Scenarios

Niewola, Adam; Podsedkowski, Leszek; Niedzwiedzki, Jakub

doi:10.1109/romoco.2019.8787362

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…The RMSE error and maximum deviation for serial, parallel, parallel-serial and the reference localization algorithms are presented in Table 2 . The results in these two figures and Table 2 clearly show that our parallel-serial algorithm outperforms the reference algorithms based on GMM [ 66 ] or PSD [ 67 ] in terms of localization accuracy.…”

Section: Resultsmentioning

confidence: 77%

“…After successful transformation into the triangular map domain, a modified version of the EKF procedure from [ 67 ] is applied. The implemented solution exploits a motion model based on the following equation:

, with

, where x , y , and z represent the position of the robot;

, and

represent the normalized quaternion q of rotation relative to the map; and

is a zero-mean Gaussian process noise vector with covariance Q .…”

Section: Methodsmentioning

confidence: 99%

“…Instead of using infinite columns projected onto a 2D plane, we use triangles that store the covariances obtained from reference LiDAR scans of the cave or mine gallery. To achieve low computational complexity, the localization algorithm introduced here uses the concept presented in [ 67 ], in which the pose correction for the robot is computed for each LiDAR measurement point separately. This reduces the computational complexity by eliminating the need for a time-consuming comparison of point clouds.…”

Section: Introductionmentioning

confidence: 99%

“…This reduces the computational complexity by eliminating the need for a time-consuming comparison of point clouds. However, unlike in [ 67 ], in our algorithm, the location is computed relative to triangles rather than surfels, which unfortunately makes the algorithm more demanding, as finding the closest triangle is a more resource-consuming task than finding a surfel on a 2D plane. Therefore, in Section 2.1 , we introduce a parallel-serial version of the proposed localization algorithm to accelerate the calculations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Parallel-Serial LiDAR-Based Localization Algorithm with Centimeter Accuracy for GPS-Denied Environments

Niedzwiedzki

Niewola

Lipiński

et al. 2020

Sensors

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In this paper, we introduce a real-time parallel-serial algorithm for autonomous robot positioning for GPS-denied, dark environments, such as caves and mine galleries. To achieve a good complexity-accuracy trade-off, we fuse data from light detection and ranging (LiDAR) and an inertial measurement unit (IMU). The proposed algorithm’s main novelty is that, unlike in most algorithms, we apply an extended Kalman filter (EKF) to each LiDAR scan point and calculate the location relative to a triangular mesh. We also introduce three implementations of the algorithm: serial, parallel, and parallel-serial. The first implementation verifies the correctness of our innovative approach, but is too slow for real-time execution. The second approach implements a well-known parallel data fusion approach, but is still too slow for our application. The third and final implementation of the presented algorithm along with the state-of-the-art GPU data structures achieves real-time performance. According to our experimental findings, our algorithm outperforms the reference Gaussian mixture model (GMM) localization algorithm in terms of accuracy by a factor of two.

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Section: Resultsmentioning

confidence: 77%

, with

, where x , y , and z represent the position of the robot;

, and

represent the normalized quaternion q of rotation relative to the map; and

is a zero-mean Gaussian process noise vector with covariance Q .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%