Before working on LiDAR uncertainty modeling, I used to think learning technical subjects was mostly about understanding formulas and memorizing concepts well enough to apply them on assignments or exams. However, after spending time working on mathematical error propagation models for LiDAR systems, I realized that real learning feels very different from simply studying information.

What is LiDAR?

LiDAR (Light Detection and Ranging) is a remote sensing technology that uses laser pulses to measure distances and generate highly detailed 3D representations of environments. It is commonly used in areas such as mapping, autonomous vehicles, forestry, and geographic analysis. Since LiDAR systems rely on multiple sensors such as GPS, IMUs, and laser scanners, small measurement errors can affect the accuracy of the final 3D points.

In the project I worked on, the goal was to estimate how uncertainty from different sensors propagates into the final coordinates of LiDAR points using mathematical modeling techniques.

Learning Through Constructivism

One of the major ideas from this unit that connected strongly with my experience was constructivism. In my case, I was working with Jacobians, covariance matrices, and error propagation equations to estimate positional uncertainty in LiDAR point clouds. Initially, many of the mathematical concepts felt abstract even though I had encountered similar ideas in math and computer science courses before.

I could follow the equations on paper, but I did not truly understand them deeply.

That changed once the math became connected to a real problem. Suddenly, derivatives and matrices were not just symbols anymore — they represented actual uncertainty coming from GPS measurements, scan angles, and sensor orientation errors. When I adjusted certain parameters and observed how they affected the uncertainty of millions of LiDAR points, the mathematics became much more meaningful.

This aligns closely with constructivist learning theory because understanding was built through experience, experimentation, and applying concepts to authentic problems rather than simply memorizing information.

Connectivism in Technical Learning

This experience also changed the way I think about where learning comes from. A large portion of my understanding did not develop through lectures alone. Instead, it came from reading research papers, exploring technical documentation, debugging code, discussing ideas with coworkers, and learning from online communities and forums.

This reflects connectivism, where knowledge is distributed across networks of people, tools, and digital resources. In technical fields such as computer science and engineering, learning often happens through connecting information from many different sources rather than relying entirely on a traditional classroom structure.

Reflection

I also realized that I personally learn best when I am actively building or solving something. Passive learning methods, such as long lectures without interaction, rarely keep me engaged for extended periods of time. In contrast, working on a difficult technical problem forces me to ask questions, test assumptions, and connect ideas from multiple disciplines including mathematics, programming, and engineering.

Overall, working on LiDAR uncertainty modeling changed the way I think about education. I now believe that the strongest learning often happens when knowledge is connected to meaningful problems, collaboration, and real-world experimentation rather than simply consuming information.

References

Ertmer, P. A., & Newby, T. J. (2018). Behaviorism, Cognitivism, Constructivism: Comparing Critical Features From an Instructional Design Perspective. In R. E. West (Ed.), Foundations of Learning and Instructional Design Technology. EdTech Books. https://edtechbooks.org/lidtfoundations/behaviorism_cognitivism_constructivism

Siemens, G. (2005). Connectivism: A Learning Theory for the Digital Age. International Journal of Instructional Technology and Distance Learning, 2(1). https://jotamac.typepad.com/jotamacs_weblog/files/connectivism.pdf

National Oceanic and Atmospheric Administration (NOAA). (n.d.). What is LiDAR? https://oceanservice.noaa.gov/facts/lidar.html