Measuring Light and Geometry Data of Roadway Environments with a Camera

Abstract

Evaluation of the conspicuity of roadway environments for their environmental impact on driving performance is vital for roadway safety. Existing meters and tools for roadway measurements cannot record light and geometry data simultaneously in a high resolution. This study introduced a new method that adopted recently developed high dynamic range (HDR) photogrammetry to measure the luminance and XYZ coordinates of millions of points across a road scene with the same device-a camera, and a MatLab code for data treatment and visualization. To validate this method, the roadway environments of a straight and flat section of Jayhawk Boulevard (482.8 m long) at Lawrence, KS and a roundabout (15.3 m in diameter) at its end were measured under clear and cloudy sky in the daytime and at nighttime with dry and wet pavements. Eight HDR images of the roadway environments under different viewing conditions were generated using the HDR photogrammetric techniques and calibrated. From each HDR image, synchronous light and geometry data were extracted in Radiance and further analyzed to identify potential roadway environmental hazards using the MatLab code (http://people.ku.edu/~h717c996/research.html). The HDR photogrammetric measurement with current equipment had a margin of errors for geometry measurement that varied with the measuring distance, averagely 23.1%-27.5% for the Jayhawk Boulevard and 9.3%-16.2% for the roundabout. The accuracy of luminance measurement was proven in the literature as averagely 1.5%-10.1%. The camera-aided measurement is fast, non-contact, non-destructive, and off the road, thus, it is deemed more efficient and safer than conventional ways using meters and tools. The HDR photogrammetric techniques with current equipment still need improvements on accuracy and speed of the data treatment.

Image pre-processing for optimizing automated photogrammetry performances

“…Although this principle seems rather evident, very few works have been published till now on the use of HDR in photogrammetry, with the exception of a validation in a laboratory experiment (Cai 2013) an application to roadway environments (Cai and Li 2014) and a general analysis for applications to strongly different illuminated Cultural Heritage structures (Ntregka et al, 2013).…”

Luminance-Corrected 3D Point Clouds for Road and Street Environments

“…When integrated to MLS system, the luminance mapping of roads could be performed parallel to MLS road inventories. The requirement for systems capable of measuring road lighting of long road segments, and producing georeferenced data has been brought up in research literature [6,37]. Integration of MLS and luminance measurement could also be applied for this purpose.…”

Does HDR Pre-Processing Improve the Accuracy of 3D Models Obtained by Means of two Conventional SfM-MVS Software Packages? The Case of the Corral del Veleta Rock Glacier

“…In this work, authors did not test the geometrical accuracy of the point clouds generated using HDR compositions [32]. Also recently, classical digital photogrammetry was fed with HDR images [34] and later applied to measure geometry and light simultaneously in roadways [35]. The results proved the usefulness of HDR approaches within the classical digital photogrammetry pipeline.…”