Testing Open3D-ML for 3D Object Detection and Segmentation

When starting out new research, my approach is usually to test different related things until enough experience allows me to begin connecting the dots. Before I could start building custom models for 3D object detection, I acquired a LiDAR and played around with some data. One next obvious step was to find out how the research worldContinue reading “Testing Open3D-ML for 3D Object Detection and Segmentation”

Normalizing (Feature Scaling) Point Clouds for Machine Learning

Continuing my work on Machine Learning with point clouds in the realm of autonomous robots, and coming from working with image data, I was faced with the following question: does 3D data need normalization like image data does? The answer is a clear YES (duh!). Normalization, or feature scaling, is an important preprocessing step for many machineContinue reading “Normalizing (Feature Scaling) Point Clouds for Machine Learning”

Simplifying Point Cloud Labeling with Contextual Images and Point Cloud Filtering

Annotating point clouds from multi-line 360° LiDAR is exceedingly difficult. Providing context in the form of camera frames and limiting the point cloud to the Field Of View (FOV) of the camera simplifies things. To achieve this, we first had to replace our old, and not so stable LiDAR mount, with a sturdier one capableContinue reading “Simplifying Point Cloud Labeling with Contextual Images and Point Cloud Filtering”

Demonstrating SEDRAD, The Self Driving Ads Robot at AppWorks.

On Saturday, May 14, 2022, we demonstrated SEDRAD at the AppWorks offices in Taipei, Taiwan. The goal was to get approval to use the robot during their upcoming Demo Day #24. The demonstration was a big success and SEDRAD is set to navigate autonomously while showing information about the participating startups in the event. AppWorksContinue reading “Demonstrating SEDRAD, The Self Driving Ads Robot at AppWorks.”