GPS World, August 2009

Mapping SURVEY p FIGURE 3 Overview of RT GPS quality monitoring tool p FIGURE 4 Overview of the multi modal architecture interfacing the scan2map system with the online quality monitoring tool HELIPOS Quality Prediction As previously mentioned the GPS positioning quality is of uppermost importance for the accuracy of the fi nal point cloud The in fl ight GPS quality assessment represents therefore a crucial step in the whole processing chain Poor GPS quality can originate from very different problems spanning poor GPS constellation cycle slips and poor signal to noise ratios interferences jamming and so on The monitoring tool uses a set of indicators FIGURE 3 for quality evaluation Solution type if a communication line between the rover and a reference station gets established and real time kinematics RTK is enabled the ambiguities can be solved onthe fly yielding the best possible estimate for achievable CP DGPS quality Analysis of the GPS constellation DOP values number of visible satellites and so on L1 L2 carrier phase tracking loop output monitoring In general L2 is more affected by cycle slips than L1 Therefore monitoring the availability of the L2 signal is important for detecting quality degradations Receiver autonomous integrity monitoring RAIM Most current GPS receivers used in kinematics are RAIMequipped RAIM enables analyzing the GPS integrity and consistency based on code measurements only Cycle slip detection on L1 Using a velocity trend method the ambiguity time difference can be computed as the temporal difference between the phase and the integrated Doppler observations Finally the status of the individual indicators is combined into one final quality flag that can be immediately presented to the system operator The quality flag has three levels Good the ambiguities should be fixed in forward and backward processing The expected 3D position accuracy should be less than 01 meters Critical the ambiguities can be resolved only partially or with low reliability The GPS position accuracy is expected to fall between 01 and 05 meters Bad no ambiguity fix possible the expected accuracy equals the float ambiguities or carrier smoothed code solution accuracy 05 meters Using these flags the covariance of the GPS point positioning used as input to the RT GPS INS integration can be adopted accordingly This yields more realistic position accuracy estimates outputted from the RT GPS INS integration which are subsequently used as input values for the RT error propagation Tool Architecture The Scan2map airborne mapping system whose development was led by EPFL TOPO combines a GPS receiver an inertial measurement unit IMU a light detection and ranging LiDAR unit and a digital camera in one solid mount The system is designed for helicopter based surveys with the sensor head suspended on its side The architecture is based on hardware and software modules and fast communication across components FIGURE 4 The software modules can run on different processors if needed The main data processing modules are GIINAV RT strapdown inertial navigation and GPS INS integration engine LIEOS RT laser point cloud georeferencing engine HELIPOS Flight monitoring and management module Embedded in LIEOS runs the LiDAR analysis module LIAN which computes the spatial distribution of the laser data derives the data extent outer bound of all strips within one flight zone and estimates data gaps zones within the extent that were not scanned completely or have not reached the minimal required point density Currently LIAN functionality has been enhanced by the capacity of performing full error propagation and computing quality indicator maps all within the flight Data handling and processing consists of three phases Data acquisition As soon as the system is started raw measurements are stored and the actual position including the GPS quality flag is transmitted to HELIPOS GIINAV www gpsworld com August 2009 GPS World 39