Stream-based Active Learning for efficient and adaptive classification of 3D objects

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 occurrence 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.

Authors

• Alexander Narr
• Rudolph Triebel
• Daniel Cremers

Keywords

• Robots
• Learning systems
• Standards
• Semantics
• Training data
• Three-dimensional displays
• Training
• Active Learning
• Object Classification
• Stream-based Active Learning
• Random Forest
• Classification Performance
• Decision Tree
• Online Learning
• Data Streams
• Mobile Robot
• Point Cloud Data
• Robot Learning
• Online Learning Algorithm
• Online Learning Methods
• Training Set
• Training Dataset
• Performance Of Method
• Support Vector Machine
• Pedestrian
• Class Labels
• Incremental Learning
• Bounding Box
• Subtree
• Original Algorithm
• Level Of Autonomy
• Leaf Node
• Tree Depth
• Offline Learning
• Decision Node
• Online Algorithm