3D Scan Registration Using Curvelet Features

Ahuja, Siddhant; Waslander, Steven L.

doi:10.1109/crv.2014.18

Cited by 4 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…General point cloud or 2D depth maps are two general approaches to achieve this. The latter may include curvelet features, as analysed in [ 18 ], assuming the range data is dense and a single viewpoint is used in order to capture the point cloud. However, it may not perform accurately for a moving LiDAR—the objective of this paper.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles

Daza

Rentero

Salinas

et al. 2020

Sensors

View full text Add to dashboard Cite

Autonomous driving systems are set to become a reality in transport systems and, so, maximum acceptance is being sought among users. Currently, the most advanced architectures require driver intervention when functional system failures or critical sensor operations take place, presenting problems related to driver state, distractions, fatigue, and other factors that prevent safe control. Therefore, this work presents a redundant, accurate, robust, and scalable LiDAR odometry system with fail-aware system features that can allow other systems to perform a safe stop manoeuvre without driver mediation. All odometry systems have drift error, making it difficult to use them for localisation tasks over extended periods. For this reason, the paper presents an accurate LiDAR odometry system with a fail-aware indicator. This indicator estimates a time window in which the system manages the localisation tasks appropriately. The odometry error is minimised by applying a dynamic 6-DoF model and fusing measures based on the Iterative Closest Points (ICP), environment feature extraction, and Singular Value Decomposition (SVD) methods. The obtained results are promising for two reasons: First, in the KITTI odometry data set, the ranking achieved by the proposed method is twelfth, considering only LiDAR-based methods, where its translation and rotation errors are 1.00 % and 0.0041 deg/m, respectively. Second, the encouraging results of the fail-aware indicator demonstrate the safety of the proposed LiDAR odometry system. The results depict that, in order to achieve an accurate odometry system, complex models and measurement fusion techniques must be used to improve its behaviour. Furthermore, if an odometry system is to be used for redundant localisation features, it must integrate a fail-aware indicator for use in a safe manner.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In [34], the results with the point-to-plane method were more precise than those with the point-to-point method, improving the precision of the measurement. The cost function to be minimised in the point-to-plane process is as follows (18):…”

Section: Normal Filtering Icpmentioning

confidence: 99%

Fail-Aware LIDAR-Based Odometry for Autonomous Vehicles

Daza

Rentero

Salinas

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…A voting/correlation procedure, such as spherical correlation, cross correlation and kernel correlation [32] is generated to search the best alignment. However, the accuracy and efficiency of these algorithms are limited by the division method [33] and it is not sensitive to constant EGI (such as a sphere) [34].…”

Section: Related Workmentioning

confidence: 99%

HEALPix-IA: A Global Registration Algorithm for Initial Alignment

Gao

et al. 2019

Sensors

View full text Add to dashboard Cite

Methods of point cloud registration based on ICP algorithm are always limited by convergence rate, which is related to initial guess. A good initial alignment transformation can sharply reduce convergence time and raise efficiency. In this paper, we propose a global registration method to estimate the initial alignment transformation based on HEALPix (Hierarchical Equal Area isoLatitude Pixelation of a sphere), an algorithm for spherical projections. We adopt EGI (Extended Gaussian Image) method to map the normals of the point cloud and estimate the transformation with optimized point correspondence. Cross-correlation method is used to search the best alignment results in consideration of the accuracy and robustness of the algorithm. The efficiency and accuracy of the proposed algorithm were verified with created model and real data from various sensors in comparison with similar methods.

show abstract