Introduction
Throughout history, location and tracking have been key to our development. From compasses to maps, humanity has advanced in navigation and exploration. Our journey began by gazing at the heavens and has evolved into harnessing technology that guides our everyday lives.
Now, as we set our sights on the skies, our advanced sensors orbiting at high speeds deliver the vital information needed to navigate our world. We have google maps and other navigation systems that help us get from point A to point B, however it's important that we understand how these positioning systems work, and how they can be used to their full potential.
Graphs and Figures
The above picture is a graphs created from the data gathered from our surveying, as well as a map plotting the data we gathered on Google Earth. We also have a 2D graph, an animation of the route taken, and a smoothed version of the path. Additionally, a 3D plot displays all data points, including altitude, to show possible errors in height readings.
The buttons below will take you to a gallery of the various methods we used to smooth the data, and the walking path is a representation of the gaussian smoothing method and displaying the path on an actual map. It shows the building we walked around and uses the Foulium library.
Sources of Error
As evident from the above figures, there are many errors and inconsistencies in the data collected. The figures, especially the one shown on Google Earth show the reasoning for the descrepencies in the data. As shown in the figure, many roofs and trees were along the route taken while surveying, which skewed the data collected, as the sensor could not get a very accurate reading, due to the interference, which prevented the satellite's signal from correctly being recorded with the sensor. This is noticable on the 3D plot as well, specifically the huge differences in height between the points, which shows that the surveying had a change in height of 50 meters, which, as seen from the Google Earth plot, is not possible.Another possible source of error, is the smoothing of the path from the 2D plot. The smoothing takes the average of the points that are close to each other, which omits some extrenous points, in an attempt to adress the afformentioned possible causes of error, though in doing so, may omit data points that are more accurate than the trend depicted. However, the inaccuracy is most likely less than one meter, which is negligable with regards to the total distance traveled, and is not as significant as the errors caused by the inaccuracies due to the buildings and other interferences to the satellite's signals.
Conclusion
This hackathon and opportunity to survey using the equipment generously provided by Hexagon has broadened our horizons in both the field of geomatics, and programming. Arguably the oldest type of engineering, gemoatics has made significant to humanity and our progress, allowing us to examine the change and topography of a multitude of landscapes. Using this equipment to do a bit of our own surveying, and writing this program has given us a new perspective on the thought, problems, and work put behind both the GPS systems used by people everyday, to the advanced navigation systems used by NASA and other organizations to map out the bottom of the Antarctic, the sea floor, and even the surface of the moon. All the hard work put in to making the navigation systems we use everyday, and the countless testing needed to ensure it works as intended, all to get us to our destination, really gives further meaning to "GPS to reduce stress", as not knowing what route to take to get to Papa John's pizza is now a thing of the past.With all this in mind, we hope that our program and the data provided are to your satisfaction, and we thank you for the opportunity that was given to us to work with this specialized equipment, along with getting help from one of the industry's most innovative companies. From our data, we determined that the graph smoothing method that was most appropriate and effective was the Gaussian smoothing method, and although there remained some sporadity within our data, since our surveying wasn't quite a straight line, as we were dodging puddles and ice while surveying, as well as going down a wrong path for a moment, which is why the SE part of the graph goes one way, and then turns back to continue down a seperate path.
Logbook
February 22, 2025
- 10:15am: Arrived at venue and received project details.
- 10:20am: Began researching sensor data syntax.
- 11:20am: Completed research and selected logs for data collection.
- 11:54am: Took initial surveying measurements.
- 11:55am: Finished surveying and began data analysis.
- 12:00pm: Researched graph types and started splitting the data.
- 12:31pm: Completed data splitting and started writing the front-end code.
- 1:00pm: Began plotting data and finalizing math functions.
- 2:30pm: Addressed issues with plot appearance.
- 3:00pm: Discovered data loss issues during saving; resurveying was needed.
- 3:19pm: Resurveyed campus to capture complete route data.
- 3:20pm: Finished surveying and analyzed the full route data.
- 3:30pm: Began analyzing new data, and began plotting various graphs using it.
- 4:00pm: Finished plotting the longitude and latitude graph.
- 4:30pm: Began to smooth the 2D plot.
- 6:00pm: Finished the 3D plot of the route.
- 7:00pm: Left from in-person component after finishing the final animation.
- 10:11pm: Implemented various smooting methods to the data, using multiple python libraries.
- 11:00pm: Started adding gallery and table of data to the website.
February 23, 2025
- 12:45am: Came up with the initial stages of adding celestial body tracking of the path (prototyped with chatGPT).
- 1:00am: Finished final layout of the website
- 6:48am: Added smoothing and images of the 3D plot, as well as captions to each image.
- 8:04am: Updated website with a background (works in local, but not on deployment).
- 8:54am: Adding comments and fixing file structure of the project.