Automatic Ground Surface Reconstruction from mobile laser systems for driving simulation engines

Craciun, Daniela; Deschaud, Jean-Emmanuel; Goulette, François

doi:10.1177/0037549716683022

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…The first modus, CAR, is to run classification algorithms on 3-D point clouds. For urban point clouds, the focus is on detecting building facades [21], road surfaces and edges [22]- [24], and distinguishing road inventory, including lighting poles and traffic signs [13], [19]. Vosselman [1] studies several postprocessing methods, and Weinmann et al [2] focus on optimal pointwise interpretation of urban 3-D point clouds.…”

Section: A Related Workmentioning

confidence: 99%

Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

Lehtola

Lehtomäki

Hyyti

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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We explore a novel paradigm for light detection and ranging (LIDAR) point classification in mobile laser scanning (MLS). In contrast to the traditional scheme of performing classification for a 3-D point cloud after registration, our algorithm operates on the raw data stream classifying the points on-the-fly before registration. Hence, we call it preregistration classification (PRC). Specifically, this technique is based on spatial correlations, i.e., local range measurements supporting each other. The proposed method is general since exact scanner pose information is not required, nor is any radiometric calibration needed. Also, we show that the method can be applied in different environments by adjusting two control parameters, without the results being overly sensitive to this adjustment. As results, we present classification of points from an urban environment where noise, ground, buildings, and vegetation are distinguished from each other, and points from the forest where tree stems and ground are classified from the other points. As computations are efficient and done with a minimal cache, the proposed methods enable new on-chip deployable algorithmic solutions. Broader benefits from the spatial correlations and the computational efficiency of the PRC scheme are likely to be gained in several online and offline applications. These range from single robotic platform operations including simultaneous localization and mapping (SLAM) algorithms to wall-clock time savings in geoinformation industry. Finally, PRC is especially attractive for continuous-beam and solid-state LIDARs that are prone to output noisy data.

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Section: A Related Workmentioning

confidence: 99%

Preregistration Classification of Mobile LIDAR Data Using Spatial Correlations

Lehtola

Lehtomäki

Hyyti

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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“…The level of resolution, i.e., the depth of subdivision, of each area depends on the density of data points in that area. Multi-resolution has been shown to be advantageous in a wide range of data structures and applications, e.g., multi-resolution mapping of geospecific model databases [1] and modeling theory [2] . MRH is intended to combine the best aspects of HEALPix with the advantages of a multi-resolution data structure, including reduced memory requirements, improved query efficiency for some types of queries, and flexible handling of proximate points.…”

Section: Introductionmentioning

confidence: 99%

A multi-resolution HEALPix data structure for spherically mapped point data

Youngren

Petty

2017

Heliyon

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Data describing entities with locations that are points on a sphere are described as spherically mapped. Several data structures designed for spherically mapped data have been developed. One of them, known as Hierarchical Equal Area iso-Latitude Pixelization (HEALPix), partitions the sphere into twelve diamond-shaped equal-area base cells and then recursively subdivides each cell into four diamond-shaped subcells, continuing to the desired level of resolution. Twelve quadtrees, one associated with each base cell, store the data records associated with that cell and its subcells.HEALPix has been used successfully for numerous applications, notably including cosmic microwave background data analysis. However, for applications involving sparse point data HEALPix has possible drawbacks, including inefficient memory utilization, overwriting of proximate points, and return of spurious points for certain queries.A multi-resolution variant of HEALPix specifically optimized for sparse point data was developed. The new data structure allows different areas of the sphere to be subdivided at different levels of resolution. It combines HEALPix positive features with the advantages of multi-resolution, including reduced memory requirements and improved query performance.An implementation of the new Multi-Resolution HEALPix (MRH) data structure was tested using spherically mapped data from four different scientific applications (warhead fragmentation trajectories, weather station locations, galaxy locations, and synthetic locations). Four types of range queries were applied to each data structure for each dataset. Compared to HEALPix, MRH used two to four orders of magnitude less memory for the same data, and on average its queries executed 72% faster.

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“…Light detection and ranging (lidar) sensors are useful for many tasks, including mapping (Nuchter et al., ; Ridene and Goulette, ; Tarel et al., ; Serna et al., ; Craciun et al., ), localisation (Narayana et al., ) and autonomous driving (Geiger et al., ). Recently, multibeam lidar sensors have appeared: they are more precise than single‐beam sensors and provide point clouds with a high point density.…”

Section: Introductionmentioning

confidence: 99%