Articles by Otto Debals
Solving Multi-Sensor Labeling Challenges in Robotics and Automotive
How to build ground-truth data, specifically for autonomous systems in the robotics and automotive industries? Otto, CEO and co-founder of Segments.ai, spoke at Auto.ai Berlin ‘24 on this topic. Otto addressed the core problems faced by teams building autonomous systems and how to solve them, focusing on data annotation [...]
Writing labeling guidelines for autonomy and multi-sensor use cases: structure and best-practice template
High-quality perception systems of autonomous vehicles & robots often leverage a large corpus of labeled ground truth data. Generating these datasets requires a well-thought-through labeling specifications or guidelines document. Publicly available datasets often share the guideline documents, for example for 2D datasets such as Cityscapes or BDD100K or for [...]
Best practices for ML teams: working with annotation providers and platforms
Computer vision technology has revolutionized multiple industries, from autonomous vehicles to advanced robotics. The foundation of these innovations lies in high-quality data annotation to build ground truth data, a critical yet complex process. This article, for computer vision engineers or data scientists, discusses some best practices for setting up and [...]
What data do you need for multi-sensor labeling?
In autonomous driving and robotics, combining camera images with lidar point cloud data can greatly improve the perception capabilities of the system. In the past, images and point clouds were often treated separately and labeled independently. Today, multi-sensor labeling brings significant advantages to the table. By combining and visualizing the [...]
