Search Results

In the world of automated driving, sensing accuracy is of the utmost importance, and proving that your sensors can do the job is serious business. This is where ground-truth labeling has an important role in Autoliv’s validation process. Currently, annotating ground-truth data is a tedious and manual effort, involving finding the important events of interest and using the human eye to determine objects from LiDAR point cloud images. We present a workflow we developed in MATLAB to alleviate some of the pains associated with labeling point cloud data from a LiDAR sensor and the advantages that the workflow provides to the labeler. We discuss the capabilities of a tool we developed to assist users in visualizing, navigating, and annotating objects in point cloud data, tracking these objects through time over multiple frames, and then using the labeled data for developing machine learning based classifiers.