This week, I worked on segmenting the point cloud data, which involved differentiating between walls and objects within a room and categorizing them into distinct models. Additionally, I developed a simple heuristic-based method to classify these segments into furniture types such as tables, chairs, and couches based on their geometric attributes. I also collaborated with Alana to integrate this new functionality into our web application. This integration involved mapping the location data of each model to its corresponding furniture type and compiling this information into a JSON file. This file will facilitate the dynamic display of placeholder furniture models at accurate positions within the web app.
The project is currently on schedule. We are currently transitioning into the testing phase. Next week, I plan to make improvements to the models based on our results from design. I will also work with my team to prepare the final project documents and get ready for the final demo.
Over the course of the semester, it was necessary to use tools and technologies to handle 3D point cloud processing, mesh manipulation, and web integration. The key tools I used were Open3D for point cloud processing and CloudCompare for mesh visualization. Open3D has been useful for tasks like segmentation, mesh generation, and transformations within 3D space. Tutorial videos were useful in quickly bringing me up to speed with the functionalities and features of Open3D. I also was able to examine how similar challenges were approached in other projects which helped me gain practical insights into what tools I could use for this project. Reading the documentation and API Guides for the RPLiDAR was also helpful for understanding how to make use of Python for the data acquisition from the sensor.