3D reconstruction of road surfaces using an integrated multi-sensory approach

Yu, Si-Jie; Sukumar, Sreenivas R.; Koschan, Andreas; Page, D. H.; Abidi, Mongi A.

doi:10.1016/j.optlaseng.2006.12.007

Cited by 95 publications

(52 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The VLS-related surveying module can be installed on various platforms, e.g., minivans, railway carts, and boats as listed in the summary review in [36] for different applications. The associated methods for data processing and information extraction concerning VLS point clouds can be referred to [37][38][39][40][41][42][43]. Technically, the common side-view mapping mechanism of VLS is similar with the conventional TLS systems, and simultaneously, VLS has the more flexible mobility than TLS.…”

Section: Open Accessmentioning

confidence: 99%

From TLS to VLS: Biomass Estimation at Individual Tree Level

et al. 2010

View full text Add to dashboard Cite

This study explores the applicability of vehicle-based laser scanning (VLS) for biomass estimation at individual tree level, since biomass serves as an essential biophysical parameter indicating tree health. Previous work suggests that terrestrial laser scanning (TLS) has been primarily validated for biomass prediction, however, in subject to laborious relocation in practice. VLS, as an advanced mode of TLS with more flexible mobility and also high sampling density, can work as a new efficient technique for surveying single trees. Combined with the positive binds between the biomass and TLS-samplings during manual defoliation, this work aims to seek the relations between biomass and VLS-samplings, by correlating the VLS-and TLS-samplings within the same crowns during natural foliation. The resulting R 2 values of the two correlations after normalization are larger than 0.88 and 0.61, respectively, and the associated root mean square errors (RMSEs) are less than 0.051 and 0.076. VLS, thus, can be validated for estimating biomass at the individual tree level, with the TLS-investigated data as a bridging reference.

show abstract

Section: Open Accessmentioning

confidence: 99%

From TLS to VLS: Biomass Estimation at Individual Tree Level

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Road monitoring agencies have witnessed a paradigm shift from camera-based systems towards laser scanners starting with the introduction of "LASER Road Imaging System" (LRIS) in 2005 [3] and similar laser systems like IBEO Laser [4] and PPS [5]. Typically, one or two illuminating laser beams reflected by the road surface are imaged by line cameras to provide 3D profile of the road.…”

Section: Existing Road Monitoring Systemsmentioning

confidence: 99%

MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data

Rajamohan

Gannu

Rajan

2015

IJGI

View full text Add to dashboard Cite

Road infrastructure in countries like India is expanding at a rapid pace and is becoming increasingly difficult for authorities to identify and fix the bad roads in time. Current Geographical Information Systems (GIS) lack information about on-road features like road surface type, speed breakers and dynamic attribute data like the road quality. Hence there is a need to build road monitoring systems capable of collecting such information periodically. Limitations of satellite imagery with respect to the resolution and availability, makes road monitoring primarily an on-field activity. Monitoring is currently performed using special vehicles that are fitted with expensive laser scanners and need skilled resource besides providing only very low coverage. Hence such systems are not suitable for continuous road monitoring. Cheaper alternative systems using sensors like accelerometer and GPS (Global Positioning System) exists but they are not equipped to achieve higher information levels. This paper presents a prototype system MAARGHA (MAARGHA in Sanskrit language means an eternal path to solution), which demonstrates that it can overcome the disadvantages of the existing systems by fusing multi-sensory data like camera image, accelerometer data and GPS trajectory at an information level, apart from providing additional road information like road surface type. MAARGHA has been tested across different road conditions and sensor data characteristics to assess its potential applications in real world scenarios. The developed system achieves higher information levels when compared to state of the art road condition estimation systems like Roadroid. The system performance in road surface type classification is dependent on the local environmental conditions at the time of imaging. In our study, the road surface type classification accuracy OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 1226 reached 100% for datasets with near ideal environmental conditions and dropped down to 60% for datasets with shadows and obstacles.

show abstract

“…Recent studies on MLS systems and their accuracy as well as environmental modelling done with MLS can be found in Barber et al (2008), Brenner (2009), Clarke (2004), El-Sheimy (2005), Früh and Zakhor (2004), Graham (2010), Haala et al (2008), Hassan and El-Sheimy (2008), Jaakkola et al (2008), Kukko et al (2007), Kukko and Hyyppä (2009), Lehtomäki et al (2010), Lin et al (2010), Lin and Hyyppä (2011), Manandhar and Shibasaki (2002), Petrie (2010), Shen et al (2008), Steinhauser et al (2008), Tao and Li (2007), Weiss and Dietmayer (2007), Yu et al (2007) and Shibasaki (2003a,b, 2005). System manufacturers have carried out and published their own tests, but few scientific publications exist where system performances are examined and compared using a test field established by and results analyzed by an independent actor.…”

Section: Introductionmentioning

confidence: 98%

Benchmarking Mobile Laser Scanning Systems Using a Permanent Test Field

Kaartinen

Kukko

Hyyppä

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Inter-Commission Working Group V/I KEY WORDS: GPS/INS, Reference Data, Systems, Laser scanning, Point Cloud, Accuracy, Mobile, Geometric ABSTRACT:The objective of the study was to benchmark the geometric accuracy of mobile laser scanning (MLS) systems using a permanent test field under good coverage of GNSS. Mobile laser scanning, also called mobile terrestrial laser scanning, is currently a rapidly developing area in laser scanning where laser scanners, GNSS and IMU are mounted onboard a moving vehicle. MLS can be considered to fill the gap between airborne and terrestrial laser scanning. Data provided by MLS systems can be characterized with the following technical parameters: a) point density in the range of 100-1000 points per m 2 at 10 m distance, b) distance measurement accuracy of 2-5 cm, and c) operational scanning range from 1 to 100 m. Several commercial, including e.g. Riegl, Optech and others, and some research mobile laser scanning systems surveyed the test field using predefined driving speed and directions. The acquired georeferenced point clouds were delivered for analyzing. The geometric accuracy of the point clouds was determined using the reference targets that could be identified and measured from the point cloud. Results show that in good GNSS conditions most systems can reach an accuracy of 2 cm both in plane and elevation. The accuracy of a low cost system, the price of which is less than tenth of the other systems, seems to be within a few centimetres at least in ground elevation determination. Inaccuracies in the relative orientation of the instruments lead to systematic errors and when several scanners are used, in multiple reproductions of the objects. Mobile laser scanning systems can collect high density point cloud data with high accuracy. A permanent test field suits well for verifying and comparing the performance of different mobile laser scanning systems. The accuracy of the relative orientation between the mapping instruments needs more attention. For example, if the object is seen double in the point cloud due to imperfect boresight calibration between two scanners, this will make especially the automatic modelling of the object much more challenging.

show abstract

3D reconstruction of road surfaces using an integrated multi-sensory approach

Cited by 95 publications

References 15 publications

From TLS to VLS: Biomass Estimation at Individual Tree Level

From TLS to VLS: Biomass Estimation at Individual Tree Level

MAARGHA: A Prototype System for Road Condition and Surface Type Estimation by Fusing Multi-Sensor Data

Benchmarking Mobile Laser Scanning Systems Using a Permanent Test Field

Contact Info

Product

Resources

About