Scan Matching by Cross-Correlation and Differential Evolution

Konecny, Jaromír; Krömer, Pavel; Prauzek, Michal; Musilek, Petr

doi:10.3390/electronics8080856

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…The rotation and translation parameters were then solved with an EM algorithm. In (Konecny et al, 2019), cross-correlation and differential evolution are used to align two scans. The cross-correlation is used between two scans as an efficient measure of scan alignment, and the differential evolution algorithm is used to look for transformation parameters that align scans.…”

Section: Related Workmentioning

confidence: 99%

Development of a Voxel Based Local Plane Fitting for Multi-Scale Registration of Sequential MLS Point Clouds

Moradi

Saadatseresht²,

Shokrzadeh³

2023

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

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Abstract. The Mobile Laser Scanner (MLS) system is one of the most accurate and fastest data acquisition systems for indoor and outdoor environments mapping. Today, to use this system in an indoor environment where it is impossible to capture GNSS data, Simultaneous Localization and Mapping (SLAM) is used. Most SLAM research has used probabilistic approaches to determine the sensor position and create a map, which leads to drift error in the final result due to their uncertainty. In addition, most SLAM methods give less importance to geometry and mapping concepts. This research aims to solve the SLAM problem by considering the adjustment concepts in mapping and geometrical principles of the environment and proposing an algorithm for reducing drift. For this purpose, a model-based registration is suggested. Correspondence points fall in the same voxel by voxelization, and the registration process is done using a plane model. In this research, two pyramid and simple registration methods are proposed. The results show that the simple registration algorithm is more efficient than the pyramid when the distance between sequential scans is not large otherwise, the pyramid registration is used. In the evaluation, by using simulated data in both pyramid and simple methods, 96.9% and 97.6% accuracy were obtained, respectively. The final test compares the proposed method with a SLAM method and ICP algorithm, which are described further.

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Section: Related Workmentioning

confidence: 99%

Development of a Voxel Based Local Plane Fitting for Multi-Scale Registration of Sequential MLS Point Clouds

Moradi

Saadatseresht²,

Shokrzadeh³

2023

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

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“…In the recent years, wireless sensors have received significant attention in automated systems [ 19 , 20 , 21 ]. The authors of [ 19 ] proposed a sensing node consisting of two magnetic field sensors on the roadside and one on the road.…”

Section: Relate Workmentioning

confidence: 99%

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Miklusis

Markevičius

Navikas

et al. 2021

Sensors

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Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

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“…Pure map matching methods using LiDAR point clouds have been developed in the robotic field. The robotic field requires indoor SLAM techniques, and the matching success rate [57][58][59][60][61][62] and computation time [63,64] are the main subjects. Since ideal map matching leads to perfect position estimation, only a few of them [57,58] clearly present positioning accuracy when using their new methods.…”

Section: B Map Matching With Predefined Mapmentioning

confidence: 99%

Landmark Attribute Analysis for a High-Precision Landmark-Based Local Positioning System

2021

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GPS signals are not reliable in urban canyons or inside tunnels. In those situations, accurate local positioning is possible through local landmarks. This paper presents the potential accuracy of such a landmark-based local positioning system and the desired attributes of landmarks influencing positioning accuracy. The analysis of the achievable accuracy and the sensitivity of the factors for precise vehicle positioning is performed using LiDAR sensor measurements in a controlled environment and a generic positioning method. The landmark-based positioning can achieve better than 0.2 m accuracy. The diameter and geometric configuration of the landmarks are the most important factors for higher accuracy. The results presented can guide the design and construction of a local positioning system in urban areas.

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Scan Matching by Cross-Correlation and Differential Evolution

Cited by 6 publications

References 52 publications

Development of a Voxel Based Local Plane Fitting for Multi-Scale Registration of Sequential MLS Point Clouds

Development of a Voxel Based Local Plane Fitting for Multi-Scale Registration of Sequential MLS Point Clouds

Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Landmark Attribute Analysis for a High-Precision Landmark-Based Local Positioning System

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