Benjamin E. Wilkinson

A Novel Approach to Terrestrial Lidar Georeferencing

Abstract This paper describes a novel method for determining theabsolute orientation of lidar point clouds using GPS measure-ments from two antennas firmly mounted on the opticalhead of a lidar scanner. The solution is linear and isderived from the non-linear georeferencing model by exploit-ing the properties of the skew-symmetric matrix. Simulationand real world experimentation using our prototype suggesta precision of about 0.05° ( 1 mrad) for the three Eulerattitude angles. The method can help alleviate problemsassociated with the conventional technique and can allowfor an increased number of practical applications forgeoreferenced terrestrial lidar. Introduction The current conventional methods for georeferencingterrestrial lidar point clouds utilize reflective targets withknown coordinates. Typically, a coordinate transformationfrom the scanner’s own coordinate system ( SOCS ) to mappingor global coordinate system ( GLCS ) can be solved using aminimum of three scanned points with known

A new approach for pass-point generation from aerial video imagery.

This paper presents a novel approach for automatically finding conjugate points between video images collected by a small autonomous unmanned aircraft. Our approach introduces the idea of saving the resampled patch from successive least-squares matching epochs and using them as templates for subsequent images. Tests show that this method is superior to using the first image as a template for all subsequent matching attempts. We show how the algorithm performs in terms of retention of points on successive images, distribution of points on the images, and utility when used for bundle adjustments in comparison with the conventional method of using the first image as a template. Our proposed method is able to match points on an average of 2.7 times as many images before failure compared with using the conventional method. This leads to stronger geometrical configuration, higher redundancy, and ultimately, significantly better bundle adjustment solutions.

Direct Georeferencing of Stationary LiDAR

Unlike mobile survey systems, stationary survey systems are given very little direct georeferencing attention. Direct Georeferencing is currently being used in several mobile applications, especially in terrestrial and airborne LiDAR systems. Georeferencing of stationary terrestrial LiDAR scanning data, however, is currently performed indirectly through using control points in the scanning site. The indirect georeferencing procedure is often troublesome; the availability of control stations within the scanning range is not always possible. Also, field procedure can be laborious and involve extra equipment and target setups. In addition, the conventional method allows for possible human error due to target information bookkeeping. Additionally, the accuracy of this procedure varies according to the quality of the control used. By adding a dual GPS antenna apparatus to the scanner setup, thereby supplanting the use of multiple ground control points scattered throughout the scanning site, we mitigate not only the problems associated with indirect georeferencing but also induce a more efficient set up procedure while maintaining sufficient precision. In this paper, we describe a new method for determining the 3D absolute orientation of LiDAR point cloud using GPS measurements from two antennae firmly mounted on the optical head of a stationary LiDAR system. In this paper, the general case is derived where the orientation angles are not small; this case completes the theory of stationary LiDAR direct georeferencing. Simulation and real world field experimentation of the prototype implementation suggest a precision of about 0.05 degrees (~1 milli-radian) for the three orientation angles.

Estimating GPS Signal Loss in a Natural Deciduous Forest Using Sky Photography

ABSTRACT Understanding how Global Positioning System (GPS) signals are influenced by vegetation structure allows for the determination of how specific technologies might be affected in certain forest environments. This study presents three different models that predict signal loss in a natural deciduous forest using fisheye photography. Relationships between terrestrial-based hemispherical sky-oriented photo (HSOP) measurements and GPS signal-to-noise ratios (SNRs) are explored. ArcGIS is used for image processing of HSOPs to rapidly estimate canopy closure (CC) at particular angles from zenith in forested areas. The difference between the observed SNR of GPS L-band signals under forest canopies to those observed in the open determines signal loss. CC values at different zenith angles inside the forest during four seasons are used to model signal attenuation. This article presents a canopy closure predictive model (CCPM), a model that includes the CCPM and incorporates the difference between the CC value in any season minus the CC in the winter, and a model that includes a seasonal component. The three models presented in this article yield adjusted R2 values between 0.60 and 0.62 and root mean square error range of 3.21 to 3.28 dB.

SectorInsights.edu—Educational Needs for Rigorous Sensor Modeling and Error Budgeting

Geomatics professionals are in a business of error analysis, or at least they should be. Acknowledging there is error (i.e., variation in observation from a “true value”) is the first step towards quantifying and minimizing it. Many can mark the locations of features or measure distances in digital photographs or other geospatial products, but those measurements are made immensely more valuable when we understand the processes that led up to making them. This includes how geospatial data are collected and the metadata that comes along with them. Specifically, estimates of uncertainty, or ranges of expected magnitudes and directions of errors, are crucial to countless geospatial applications. Our understanding of the uncertainty in geospatial measurements is what sets us apart. For example, some would say a key difference between the photogrammetric and computer vision fields is photogrammetry’s emphasis on geometric accuracy, uncertainty estimation, and preference for model rigor over computer vision’s preoccupation with speed and simplicity. The concepts and practical applications of rigorous sensor modeling and error budgeting (i.e., how much unexplainable variation we are willing to accept from the “true value”) are crucial to the professional and educational realms of the geospatial world. An error budget can simply be a list of errors that accumulate along the collection and processing pipeline and induce error in the final product, or, more valuably, be represented in a mathematical model of the collection and processing algorithms and their accompanying errors. Central to this mathematical model is the sensor model. A sensor model is defined as the relationship linking object space coordinates and sensor space measurements. Many refer to a rigorous sensor model, meaning the model attempts to closely capture the physical phenomena occurring during acquisition, while maintaining a level of complexity that makes the model useable. This brings to mind the statistician George Box’s quote:

Dual-Antenna Terrestrial Laser Scanner Georeferencing Using Auxiliary Photogrammetric Observations

Terrestrial laser scanning typically requires the use of artificial targets for registration and georeferencing the data. This equipment can be burdensome to transport and set up, representing expense in both time and labor. Environmental factors such as terrain can sometimes make target placement dangerous or impossible, or lead to weak network geometry and therefore degraded product accuracy. The use of additional sensors can help reduce the required number of artificial targets and, in some cases, eliminate the need for them altogether. The research presented here extends methods for direct georeferencing of terrestrial laser scanner data using a dual GNSS antenna apparatus with additional photogrammetric observations from a scanner-mounted camera. Novel combinations of observations and processing methods were tested on data collected at two disparate sites in order to find the best method in terms of processing efficiency and product quality. In addition, a general model for the scanner and auxiliary data is given which can be used for least-squares adjustment and uncertainty estimation in similar systems with varied and diverse configurations. We found that the dual-antenna system resulted in cm-level accuracy practical for many applications and superior to conventional one-antenna systems, and that auxiliary photogrammetric observation significantly increased accuracy of the dual-antenna solution.