Abstract-We present a new Active Learning approach for classifying objects from streams of 3D point cloud data. The major problems here are the non-uniform occurence of class instances and the unbalanced numbers of samples per class. We show that standard online learning methods based on decision trees perform comparably bad for such data streams, which are however particularly relevant for mobile robots that need to learn semantics persistently. To address this, we use Mondrian forests (MF), a recent online learning algorithm that is independent on the data order. We present an extension of that algorithm and show that MF are less overconfident than standard Random Forests. In experiments on the KITTI benchmark, we show that this leads to a substantially improved classification performance for data streams, rendering our approach very attractive for lifelong robot learning applications.
Another approach described in REF applies active learning based on streams of 3D point cloud data to train a robot classifying objects.
Stream-based Active Learning for efficient and adaptive classification of 3D objects
2016 IEEE International Conference on Robotics and Automation (ICRA)
2016 IEEE International Conference on Robotics and Automation (ICRA)
Computer Science