Mobile mapping system performance - an analysis of the effect of laser scanner configuration and vehicle velocity on scan profiles

When a laser scanner is mounted on a moving platform and combined with a GNSS receiver and inertial navigation system, it is capable of producing millions of geo-referenced points which can then be used to create near-3D models. The development of processing algorithms for these point clouds has largely been the focus of the research community to date. However, given an arbitrary known static object positioned at a specific distance away from a mobile mapping system (MMS) the resolution of the resulting point cloud that will describe that object is unknown. This is the underlying limit of point cloud processing algorithms. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. Our previous work has demonstrated our initial investigations into the effect that a scanner position, configuration and operating speed has on scan lines - both in profile spacing and scan line orientation at varying vehicle speeds. This paper focuses on the combined effect on profiles of both vertical and horizontal rotations of the scanner, explores in greater detail the effect on scan line orientation caused by vehicle motion and also incorporates point spacing as a function of range into our model. As with our previous work, we will develop a system to calculate this information and then verify our equations and analysis by comparing our simulated data to the point cloud data collected by our XP-1 mobile mapping system

Calculating the effect of dual-axis scanner rotations and surface orientation on scan profiles

The large volumes of point cloud data collected by a Mobile Mapping System(MMS) equipped with a laser scanner have attracted the attention of the research community, primarily towards developing automated algorithms to help when processing this data. This has resulted in insufficient attention being paid to quantifying the capabilities of these systems, and due to the relative youth of this technology there is no concrete understanding of the point density that different hardware configurations and operating parameters will exhibit on objects at specific distances. Obtaining the required point density for a project impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. Lack of understanding of these systems makes defining point density in project specifications a complicated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. We have previously demonstrated the effect that scanner orientation in one axis, scanner configuration and scanner operating speed have on scan profiles. We have also focused on the effect on scan profiles of the combined vertical and horizontal rotations of the scanner (dual-axis rotations) and also incorporated point spacing for planar surfaces at different scanner mirror speeds, pulse repetition rates and field of view as a function of range into our model. The subject of this paper is to investigate the effect that a dual-axis scanner rotation has on profile spacing and to design a theoretical system to calculate the angular change on profiles exhibited on horizontal and vertical surfaces for different system configurations. The second goal of the research presented in this paper is to include in our calculations a method for incorporating surfaces that are not parallel to the direction of travel or that are not perfectly vertical, such as walls facing away from the road or sloped surfaces. Profile angle impacts on profile spacing and is a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings, all important features in asset management surveys. A number of tests were designed to investigate these issues and the results show that these tests have justified our methods, but it has been made apparent that vehicle dynamics play a larger role than anticipated

MIMIC : Mobile Mapping Point Density Calculator

The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that differ- ent hardware configurations and operating parameters will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. A limited un- derstanding of the capabilities of these systems means that defining point density in project specifications is a compli- cated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at spec- ified distances. We have previously demonstrated the capa- bilities of our system for calculating point spacing, profile angle and profile spacing individually. Each of these ele- ments are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings - all important features in asset management surveys. This paper will introduce the current version of the MobIle Map- ping point densIty Calculator (MIMIC), MIMIC’s visuali- sation module and finally discuss the methods employed to validate our work

Initial Results From European Road Safety Inspection (eursi) Mobile Mapping Project

Mobile mapping systems are becoming a popular method for collecting high quality near 3D information of terrestrial scenes. Modern mobile mapping systems can produce millions of georeferenced points per minute. These can be used to gather quantitative information about surfaces and objects. With this geospatial data it is becoming possible to segment and extract the road surface. In this paper, we will detail a novel LIDAR based road edge extraction algorithm which is applicable to both urban and rural road sections

Mobile terrestrial LiDAR data-sets in a Spatial Database Framework

Mobile Mapping Systems (MMS) have become important and regularly used platforms for the collection of physical-environment data in commercial and governmental spheres. For example, a typical MMS may collect location, imagery, video, LiDAR and air quality data from which models of the built-environment can be generated. Numerous approaches to using these data to generate models can be envisaged which can help develop detailed knowledge in the monitoring, maintanence and development of our built-environment. In this context, the efficient storing of this raw spatial data is a significant problem such that bespoke and dynamic access is possible for the generation of modeling requirements. This fundamental requirement of managing these data, where upwards of 40 gigabytes per hour of spatial-information can be collected from an MMS survey, poses significant challanges in data management alone. Existing methodologies mantain bespoke, survey oriented approaches to data management and model generation where the original MMS spatial data is not generally used or available outside these requirements. Thus, there is a need for an MMS data management framework where effective storage and access solutions can hold this information for use and analysis in any modeling context. Towards this end we detail our storage solution and the experiments where the procedures for high volume navigation and LiDAR MMS-data loading are analysed and optimised for minimum upload times and maximum access efficiency. This solution is built upon a PostgreSQL Relational Database Management System (RDBMS) with the PostGIS spatial extension and pg bulkload data loading utility

LiDAR data management pipeline; from spatial database population to web-application visualization

While the existence of very large and scalable Database Management Systems (DBMSs) is well recognized, it is the usage and extension of these technologies to managing spatial data that has seen increasing amounts of research work in recent years. A focused area of this research work involves the handling of very high resolution Light Detection and Ranging (LiDAR) data. While LiDAR has many real world applications, it is usually the purview of organizations interested in capturing and monitoring our environment where it has become pervasive. In many of these cases, it has now become the de facto minimum standard expected when a need to acquire very detailed 3D spatial data is required. However, significant challenges exist when working with these data sources, from data storage to feature extraction through to data segmentation all presenting challenges relating to the very large volumes of data that exist. In this paper, we present the complete LiDAR data pipeline as managed in our spatial database framework. This involves three distinct sections, populating the database, building a spatial hierarchy that describes the available data sources, and spatially segmenting data based on user requirements which generates a visualization of these data in a WebGL enabled web-application viewer. All work presented is in an experimental results context where we show how this approach is runtime efficient given the very large volumes of LiDAR data that are being managed

Automated road extraction from terrestrial based mobile laser scanning system using the GVF snake model

Accurate extraction and reconstruction of route corridor features from geospatial data is a prerequisite to effective management of road networks for engineering, safety and environmental applications. High quality road geometry and road side features can now be extracted from dense point cloud LiDAR data, recorded by modern day Mobile Mapping Systems. This valuable route network information is gaining the attention of road safety and maintenance engineers. Road points are needed to be correctly identified, classified and extracted from LiDAR data before reconstructing intrinsic road geometry and road-side infrastructure. In this paper, we present a method to automatically extract the road from terrestrial based mobile laser scanning system using the GVF (Gradient Vector Flow) snake model. A snake is an energy minimizing spline that moves towards the desired feature or object boundary under the influence of internal forces within the curve itself and external GVF forces derived typically from 2D imaging data by minimizing certain energy such as edges or high frequency information. In our novel method, we initialise the snake contours over point cloud data based on the trajectory information produced by the MMS navigation sub-system. The internal energy term provided to the snake contour is based on adjusting the intrinsic properties of the curve, such as elasticity and bending, whilst the GVF energy and constraint energy terms are derived from the LiDAR point cloud attributes. Our method primarily differs from the traditional snake models in initialisation and in deriving the energy terms from the 3D LiDAR data

Snake energy analysis and result validation for a mobile laser scanning data-based automated road edge extraction algorithm

© 2008-2012 IEEE. The negative impact of road accidents cannot be ignored in terms of the very sizeable social and economic loss. Road infrastructure has been identified as one of the main causes of the road accidents. They are required to be recorded, located, measured, and classified in order to schedule maintenance and identify the possible risk elements of the road. Toward this, an accurate knowledge of the road edges increases the reliability and precision of extracting other road features. We have developed an automated algorithm for extracting road edges from mobile laser scanning (MLS) data based on the parametric active contour or snake model. The algorithm involves several internal and external energy parameters that need to be analyzed in order to find their optimal values. In this paper, we present a detailed analysis of the snake energy parameters involved in our road edge extraction algorithm. Their optimal values enable us to automate the process of extracting edges from MLS data for tested road sections. We present a modified external energy in our algorithm and demonstrate its utility for extracting road edges from low and nonuniform point density datasets. A novel validation approach is presented, which provides a qualitative assessment of the extracted road edges based on direct comparisons with reference road edges. This approach provides an alternative to traditional road edge validation methodologies that are based on creating buffer zones around reference road edges and then computing quality measure values for the extracted edges. We tested our road edge extraction algorithm on datasets that were acquired using multiple MLS systems along various complex road sections. The successful extraction of road edges from these datasets validates the robustness of our algorithm for use in complex route corridor environments

Removing the twin image in digital holography by segmented filtering of in-focus twin image

We propose and investigate a new digital method for the reduction of twin-image noise from digital Fresnel holograms. For the case of in-line Fresnel holography the unwanted twin is present as a highly corruptive noise when the object image is numerically reconstructed. We propose to firstly reconstruct the unwanted twin-image when it is in-focus and in this plane we calculate a segmentation mask that borders this in focus image. The twin-image is then segmented and removed by simple spatial filtering. The resulting digital wavefield is the inverse propagated to the desired object image plane. The image is free of the twin-image resulting in improved quality reconstructions. We demonstrate the segmentation and removal of the unwanted twin-image from in-line digital holograms containing real-world macroscopic objects. We offer suggestions for its rapid computational implementation

A Practical Guide to Digital Holography and Generalized Sampling

The theorems of Nyquist, Shannon and Whittaker have long held true for sampling optical signals. They showed that a signal (with finite bandwidth) should be sampled at a rate at least as fast as twice the maximum spatial frequency of the signal. They proceeded to show how the continuous signal could be reconstructed perfectly from its well sampled counterpart by convolving a Sinc function with the sampled signal. Recent years have seen the emergence of a new generalized sampling theorem of which Nyquist Shannon is a special case. This new theorem suggests that it is possible to sample and reconstruct certain signals at rates much slower than those predicted by Nyquist-Shannon. One application in which this new theorem is of considerable interest is Fresnel Holography. A number of papers have recently suggested that the sampling rate for the digital recording of Fresnel holograms can be relaxed considerably. This may allow the positioning of the object closer to the camera allowing for a greater numerical aperture and thus an improved range of 3D perspective. In this paper we: (i) Review generalized sampling for Fresnel propagated signals, (ii) Investigate the effect of the twin image, always present in recording, on the generalized sampling theorem and (iii) Discuss the effect of finite pixel size for the first time