Robots and other complex machines are usually equipped with a wide variety of sensors. Often, when performing tasks such as driving autonomously or collecting data for offline processing, it is useful to synchronize the different sensors. For example, in my work with 3D Machine Learning, I had to collect a dataset consisting of LiDAR pointContinue reading “Synchronizing LiDAR and Camera Data for Offline Processing Using ROS”
Tag Archives: ai
3D Object Detection with Open3D-ML and PyTorch Backend
In previous articles, I described how I used Open3D-ML to do Semantic Segmentation on the SemanticKITTI dataset and on my own dataset. Now it is time to move to another important aspect of the Perception Stack for Autonomous Vehicles and Robots, which is Object Detection from Point Clouds. Make sure to install Open3D-ML with PyTorchContinue reading “3D Object Detection with Open3D-ML and PyTorch Backend”
Filtering a Point Cloud to Match the Field of View of the Camera
In a previous post, I described why and how I was collecting a Point Clouds dataset. My setup is depicted in the image above, where a 360°, 32-beam LiDAR is placed above a stereo camera. One of the steps mentioned in the article was to crop (or filter) the Point Cloud to only show points approximatelyContinue reading “Filtering a Point Cloud to Match the Field of View of the Camera”
Semantic Segmentation with Open3D-ML, PyTorch Backend, and a Custom Dataset
Note: Instructions to download, run, and troubleshoot the code introduced in this article are provided at the end. As part of my experimentation with Open3D-ML for Point Clouds, I wrote articles explaining how to install this library with Tensorflow and PyTorch support. To test the installation, I explained how to run a simple Python script to visualize a labeledContinue reading “Semantic Segmentation with Open3D-ML, PyTorch Backend, and a Custom Dataset”
Installing Open3D-ML for 3D Computer Vision with PyTorch
In a previous post, I introduced my reasons to test Open3D-ML and the steps to install it with TensorFlow as the backend. In this post, I go over the steps to install the same library with PyTorch as the backend. Many of the steps are similar but there are some important differences. I hope thisContinue reading “Installing Open3D-ML for 3D Computer Vision with PyTorch”
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”
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.”
Testing 3D Annotation Tools
Before you can train a supervised Deep Learning model, you must first label your data. Today I am testing Intel OpenVINO’s CVAT and MATLAB’s Lidar Labeler annotation tools for 3D data. First impressions, CVAT makes it easier to navigate the point cloud, but a small bug makes it hard to place the initial cuboid, makingContinue reading “Testing 3D Annotation Tools”
SEDRAD, The Self-Driving Ads Robot is Finally Here
I am pleased to announce that the first version of SEDRAD, The Self-Driving Ads Robot, is finally here. I have released it as part of the final submission of the OpenCV Spatial AI Competition #Oak2021. Watch the video to learn more about what SEDRAD is capable of doing, and if you have any questions, don’tContinue reading “SEDRAD, The Self-Driving Ads Robot is Finally Here”
ROS+Oak-D: Turning a depth image into a 2-D laser scan for obstacle avoidance.
When we applied to the #OpenCV Spatial AI Competition #Oak2021, the very first issue we told the organizers we were going to solve using an Oak-D stereo camera was the inability of our robot to avoid obstacles located lower than the range of its 2D lidar. Back then we had no idea how we wereContinue reading “ROS+Oak-D: Turning a depth image into a 2-D laser scan for obstacle avoidance.”
Soul Hackers Labs 