Extract 3D points from raw, full waveform LiDAR files. Get more points and more accurate results even at high altitude and in difficult cases with low vegetation. High throughput achieved using efficient algorithms and an optimized parallel implementation.
– Raw waveforms: Riegl SDF
– Point clouds: Riegl RXP or SDC
– [position & orientation file, geodetic]
– [calibration file]
– LAS/LAZ 1.2-1.4 or ASCII file [extra attributes + uncertainty]
– [PulseWaves waveforms]
– [exported trajectory, footprints, transects, stat files]
Direct decoding of Riegl SDF files (scanner types 560 to 1560)
Single pass decoding, processing, georeferencing, LAS output
Single pass georeferencing for RXP or SDC point clouds
Automatic MTA (multiple times around) resolution
Optimal pre-calibrated sensor parameters
Channel fusion and pulse ordering
Extraction robust to peak overlaps and noise
Underground false alarm suppression using advanced modeling
Channel cross-talk suppression
Fast, accurate waveform decomposition
Ultra-fast extraction options for quick preview
Physically meaningful target attribute extraction
Uncertainty attribute export options
Basic outlier filtering (high/low point elimination)
Various correction options (intensity, IBRC, width…)
Footprint, transect, center and trajectory export
Trajectory file only required to georeference SDF data
Bayesian inference is not only about producing fuzzy quantities! It actually ensures that results have maximum accuracy, compared to ad-hoc or classical approaches. Proper modeling ensures reduced errors and is effective in waveform processing. High detection rates can be achieved, allowing you to fly higher and cut collection costs!
Optimal data fusion, or swath combination, helps produce highest possible accuracy elevation models given the collected data.
Manually tuning essential processing parameters is no longer necessary. Topography smoothness, outlier frequency, that’s the algorithm’s problem, not yours!
Deciding at what scan angle to cut off data is over! Uncertainty propagation ensures a proper weighting is applied to each data point, avoiding throwing away precious information and significant portions of the input point clouds.
Managing uncertainty properly allows not only to propagate errors, but to preserve information by using error structure (e.g. covariances). Understanding how uncertainty affects elevation is essential for further shape analysis and change detection. This does not come with the data, but we help you compute it!