GNSS Tersus Oscar – Updated driver to resolve height issue + settings page loading timeĮmlid Reach Receivers – Updated driver to address Bluetooth reconnection issuesĬHC LT700H – Updated driver to resolve connection issue Shapefile Import/Export – Shapefile import/export to support working with your GIS software.Report Feedback -Built-in report feedback option to allow for users to easily communicate with development to report issues and request new features.ĬOGO Pt…Pt Inversing - New enhancementto the COGO calculation to allow usersto easily inverse direction and distances while using the COGO calculation functions to compute new points.ĭirectionalCurrent Position – Support to display the current direction the user is facing on the map view using the internal compass on their device to help indicate which way they are orientated. Also provides the ability to import and quickly extract features from a shapefile to produce a feature list.
GIS AttributeCollection - FieldGenius for Android now has support for allowing users to tag attribute information with their point/line observations.įeature File Manager - Provides the ability to manage existing feature files and create new ones within the application for GIS attribute collection.
Here is how all the baselines look in EzSurv after the network-adjustments:īelow is the text from their update notice:įieldGenius for Android Update 1.8.2 - Release Notes March 3, 2021 After the network-adjustment, this error was now down to 5 cm vertical.
The resulting will be an “averaged” point with much higher confidence!Īs an example the BS2 and #6 points are particular tricky, and even though BS2 point was observed for over an hour, all 3 baselines-results came in as Floats, with an RMS of. I used the Low-rms Fixed solutions as references for the less good observations.
Using these multiple baselines, EzSurv is capable of doing a Least Square Adjustment on the data, using user-defined references. And that’s where the multiple baselines come into play. This lack of fix resulting in some solutions being Float and some even Single. However, as expected, even though I had 40+ mins of obs-time on each point, not all would fix anyhow. With a baseline of just 4 km, I could easily use the NTRIP to fix numerous point on the site, as well as my own local base. The resulting static Rinex files where then fed into EzSurv for the post-processing step.įor absolute reference I used a nearby NTRIP-station from RTKconnect (which is a RS2 based NTRIP network here in Denmark). So now with a lot of data collected, it is time to put it all together! I had collected 4 long, continuous logs, and then split them up into the different observed points using RTKLib provided by Emlid. Additionally, careful planning will make sure that excessive walking-around is prevented. This exercise requires a lot of planning though to make sure overlap between the different points are optimized, so they have at least 20 minutes of overlap.
Given the obs-times, this will allow me multiple baselines for each point, which makes for a rigid structure of interlined baselines. So instead I planned on using 1 Base and then 3 static rovers. With 40 minutes on each point and 19 points, I wouldn’t be able to complete 1 workday with a classic Rover/Base pair. So to be sure to get a good average and the ability to fix even with L1 only, I planned for at least 40 minutes on each point. Plenty of obstructions.įrom previous visits I know that for the challenging positions near the trees or structures even 15 mins obs times wouldn’t be enough. Another side has a large metal structure in 13 meters height. On one side it has 25 meter high trees, that in the summer-time partially covers the side of the stockpile. The jobsite is very challenging from a GNSS perspective to say the least. Like when you have trees and metal building all around, and you still need precise GCP’s without a total station.įor a stockpile volume jobsite I installed a perimeter of large screws around the stockpiles, so I can lay out GCP’s quickly every time I visit the site, which is regularly.