Charles K. Toth

Topographic laser ranging and scanning : principles and processing

Introduction to Laser Ranging, Profiling, and Scanning, G. Petrie and C. K. Toth Airborne and Spaceborne Laser Profilers and Scanners, G. Petrie and C. K. Toth Terrestrial Laser Scanners, G. Petrie and C. K. Toth LiDAR Systems and Calibration, A. Wehr Pulsed Laser Altimeter Ranging Techniques and Implications for Terrain Mapping, D. Harding Georeferencing Component of LiDAR Systems, N. El-Sheimy Waveform Analysis for Small-Footprint Pulsed Laser Systems, U. Stilla and B. Jutzi Strip Adjustment and Registration, C. K. Toth Accuracy, Quality Assurance, and Quality Control of LiDAR Data, A. Habib Management of LIDAR Data, L. Graham LiDAR Data Filtering and DTM Generation, N. Pfeifer Forest Inventory Using Small-Footprint Airborne Lidar, J. Hyyppa, H. Hyyppa, X. Yu, H. Kaartinen, A. Kukko, and M. Holopainen Integration of LiDAR and Photogrammetric Data: Triangulation and Ortho Rectification, A. Habib Feature Extraction from Lidar Data in Urban Areas, F. Bretar Building Extraction from LiDAR Point Clouds Based on Clustering Techniques, J. Shan and A. Sampath Building and Road Extraction by LiDAR and Imagery, F. Rottensteiner and S. Clode A Data-Driven Method for Modeling 3D Building Objects Using a Binary Space Partitioning Tree, G. Sohn, X. Huang, and V. Tao A Framework for Automated Construction of Building Models from Airborne LIDAR Measurements, K. Zhang Quality of Buildings Extracted from Airborne Laser Scanning Data: Results of an Empirical Investigation on 3D Building Reconstruction, E. Gulch, H. Kaartinen, and J. Hyyppa Index

Improvement of lidar data accuracy using lidar-specific ground targets

With recent advances of lidar technology, the accuracy potential of lidar data has significantly improved. State-of-the-art lidar systems can achieve 2 to 3 cm ranging accuracy under ideal conditions, which is the accuracy level required by engineering scale mapping. However, this is also the accuracy range that cannot be realized by routine navigation-based direct sensor platform orientation. Furthermore, lidar systems are highly integrated multi-sensor systems, and the various components, as well as their spatial relationships, introduce different errors that can degrade the lidar data accuracy. Even after careful system calibration, including individual sensor calibration and sensors intra-calibration, certain errors in the collected data can still be present. These errors are usually dominated by navigation errors and cannot be totally eliminated without introducing absolute control information into the lidar data. Therefore, to support applications that require extremely high, engineering scale mapping accuracy, such as transportation corridor mapping, we propose the use of lidar-specific ground targets. Simulations were performed to determine the most advantageous lidar target design and targets were fabricated based upon the simulation results. To investigate the potential of using control targets for lidar data refinement, test flights were carried out with different flight parameters and target distributions. This paper provides a description of the optimal lidar target design, the target identification algorithm, and a detailed performance analysis, including the investigation of the achievable lidar data accuracy improvement using lidar-specific ground control targets in the case of various target distributions and flight parameters.

Sensor integration in airborne mapping

The rapid technological developments of the 1990s have completely redefined the mapping practice. In less than a decade, digital techniques have come to outnumber traditional analog data acquisition and processing methods. Supported by an unprecedented demand for large-volume, accurate spatial data, these new digital techniques emerged as dominant mapping technologies by the end of the decade. Two key components of this emerging technology are the electronic sensor-based digital camera and GPS/INS-based direct platform orientation. In fact, these new platform orientation systems are rapidly becoming a core component of modern airborne mapping and remote sensing systems. To achieve the ultimate potential of the new hardware components, in other words to provide the highest mapping accuracy, the sensors should be carefully modeled and calibrated accordingly both individually and as an integrated sensor suite. The paper discusses the developments achieved by a totally digital airborne mapping system, including concept, system architecture, calibration, and performance results.

A Robust Solution to High-Accuracy Geolocation: Quadruple Integration of GPS, IMU, Pseudolite, and Terrestrial Laser Scanning

Reliable and accurate geolocation is essential for airborne and land-based remote sensing applications. The detection, discrimination, and remediation of unexploded ordnance (UXO) and other munitions and explosives of concern (MEC) using the currently available detection and geolocation technologies often yield unsatisfactory results, failing to detect all MEC present at a site or to discriminate between MEC and nonhazardous items. Thus, the goal of this paper is to design and demonstrate a high-accuracy geolocation methodology that will address centimeter-level relative accuracy requirements of a man-portable electromagnetic (EM) sensor system in open and impeded environments. The proposed system design is based on the tight quadruple integration of the Global Positioning System (GPS), the inertial measurement unit (IMU) system, the terrestrial radio-frequency (RF) system pseudolite (PL), and terrestrial laser scanning (TLS) to support high-accuracy geolocation for a noncontact EM mapping system in GPS-challenged environments. The key novel component of the proposed multisensor system is the integration of TLS that can provide centimeter-level positioning accuracy in a local frame and thus enables a GPS/IMU/PL-based navigation system to achieve both high absolute and relative positioning accuracy in GPS-impeded environments. This paper presents the concept design of the quadruple integration system, the algorithmic approach to data integration with a special emphasis on TLS integration with GPS/IMU/PL, and the performance assessment based on real data, where centimeter-level relative geolocation accuracy is demonstrated during the GPS signal blockage.

Pedestrian Tracking and Navigation Using Neural Networks and Fuzzy Logic

The main goal of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate GPS, micro-electro-mechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the initial performance analyses of the personal navigator prototype1, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. To facilitate this functionality, the adaptive knowledge system, based on the Artificial Neural Networks (ANN) and Fuzzy Logic, is trained during the GPS signal reception and used to maintain navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL) are estimated during the system calibration period, then the predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3-5 m CEP (circular error probable) 50%.

Pedestrian tracking and navigation using an adaptive knowledge system based on neural networks

Abstract The primary objective of the research presented here is to develop theoretical foundations and implementation algorithms, which integrate the Global Positioning System (GPS), micro-electromechanical inertial measurement unit (MEMS IMU), digital barometer, electronic compass, and human pedometry to provide navigation and tracking of military and rescue ground personnel. This paper discusses the design, implementation and the performance analyses of the personal navigator prototype, with a special emphasis on dead-reckoning (DR) navigation supported by the human locomotion model. The adaptive knowledge system, based on the Artificial Neural Networks (ANN), is implemented to support this functionality. The knowledge system is trained during the GPS signal reception and is used to support navigation under GPS-denied conditions. The human locomotion parameters, step frequency (SF) and step length (SL), are extracted from GPS-timed impact switches (step frequency) and GPS/IMU data (step length), respectively, during the system calibration period. SL is correlated with several data types, such as acceleration, acceleration variation, SF, terrain slope, etc. that constitute the input parameters to the ANN-based knowledge system. The ANN-predicted SL, together with the heading information from the compass and gyro, support DR navigation. The current target accuracy of the system is 3–5 m CEP (circular error probable) 50%.

A Piecewise Approach to Epipolar Resampling of Pushbroom Satellite Images Based on RPC

Epipolar line determination and image resampling are important steps for stereo image processing. Unlike frame cameras that have well-known epipolar geometry, the pushbroom camera does not produce straight epipolar lines and the epipolar pair does not exist for the entire scene. These properties make it difficult to establish epipolar geometry of the pushbroom camera for epipolar image resampling. Therefore, some researchers have adopted approximate models to avoid these problems. In this study, a new method for the conjugate epipolar curve pair determination and epipolar resampling of spaceborne pushbroom images based on RPC is proposed. The proposed method assumes that the conjugate epipolar curve pairs exist approximately for the local scene area and the global epipolar pairs can also exist if the local pairs are sequentially linked. Then, epipolar image resampling is established by reassigning the generated conjugate epipolar curve pair points to satisfy the epipolar resampling image condition. Ikonos stereo images are tested for the evaluation, and the proposed method showed a maximum y-parallax of 1.25 pixels for manually measured tie points, while the resampling method by the parallel projection model showed a maximum of 4.59 pixels.

From mobile mapping to telegeoinformatics: Paradigm shift in geospatial data acquisition, processing, and management

Technological advances in positioning and imaging sensors, combined with the explosion in wireless mobile communication systems that occurred during the last decade of the twentieth century, practically redefined and substantially extended the concept of mobile mapping. The advent of the first mobile mapping systems (MMS) in the early 1990s initiated the process of establishing modern, virtually ground-control-free photogrammetry and digital mapping. By the end of the last decade, mobile mapping technology had made remarkable progress, evolving from rather simple land-based systems to more sophisticated, real-time multitasking and multisensor systems, operational in land and airborne environments. New specialized systems, based on modern imaging sensors, such as CCD (charge-coupled device) cameras, lidar (Light Detection and Ranging) and hyperspectral/multispectral scanners, are being developed, aimed at automatic data acquisition for geoinformatics, thematic mapping, land classification, terrain modeling, emergency response, homeland security, etc. This paper provides an overview of the mobile mapping concept, with a special emphasis on the MMS paradigm shift from the post-mission to near-real-time systems that occurred in the past few years. A short review of the direct georeferencing concept is given, and the major techniques (sensors) used for platform georegistration, as well as the primary radiolocation techniques based on wireless networks, are presented. An overview of the major imaging sensors and the importance of multisensor system calibration are also provided. Future perspectives of mobile mapping and its extension towards telegeoinformatics are also discussed. Some examples of mobile geospatial technology used in automatic object recognition, real-time highway centerline mapping, thematic mapping, and city modeling with lidar and multispectral imagery are included.

Terrain-based navigation: Trajectory recovery from LiDAR data

The need for complementary technologies to support navigation in GPS-challenged environment is rapidly growing in both outdoor and indoor environments. Remote sensing/mapping sensor performance continues to advance resulting in better spatial and temporal resolution of the acquired geospatial data, which, combined with the increasing hardware performance, can be available real-time or near real-time, and thus could be utilized in forming or improving the navigation solution. Terrain-based navigation has been used for a number of years to navigate airborne platforms, but the continuous exchange of precise geolocation information between the imaging and navigation modules to improve the overall error calibration is a novel idea, which should significantly increase the systempsilas fault tolerance in a variety of situations. The typical navigation solutions for airborne mapping systems are currently based on a GPS or integrated GPS/IMU systems, supporting usually a single imaging sensor, with no feedback between the sensory data processing filters. Most of the research in terrain-based navigation proposes the use of optical measurements from airborne imagery, although the concept of exploring LiDAR-based terrain navigation has also been reported. This paper is concerned with obtaining navigation data from LiDAR, and investigates the feasibility of the airborne trajectory recovery method based on LiDAR data using reference terrain surface models. If GPS signals are lost, the coordinates of LiDAR points can still be computed using the inertial-only solution, however, with errors growing in time. If reference surface data exists, they can be used to recover the LiDAR sensor trajectory by surface matching as long as the IMU drift is under a certain threshold. To assess the performance of the proposed method both simulated LiDAR data were used and an analysis oF the feasibility of the method is provided.

Redefining the Paradigm of Modern Mobile Mapping: An Automated High-Precision Road Centerline Mapping System

This article describes the high-precision land-based integrated mapping system that was developed at The Ohio State University to support road centerline mapping operations at the Ohio Department of Transportation District 1 Office. The two key components of the custom-designed system are a high-precision integrated global positioning system and inertial navigation system (GPS/INS) and a fully digital and automated imaging subsystem. The van-based mapping system was designed to deliver the road centerline positions at subdecimeter accuracy in a highly automated manner, with limited human interaction and in near real time. The authors present the systems concept and design, followed by an individual performance evaluation of the navigation and imaging components; and finally road test results (in and around the OSU campus) representing an operational environment, are also reported. The authors conclude that the tightly coupled GPS/INS navigation system has shown excellent performance under good to moderate GPS signal reception. They briefly discuss strategies to incorporate when GPS signals are weak or lost.