Online learning for scene segmentation with laser-constrained CRFs

Abstract

Scene understanding is a crucial requirement for robot navigation. Conditional Random Fields (CRF) are commonly used to solve the scene labelling problem since they represent contextual information efficiently and provide efficient inference methods. However, when a robot navigates through an unknown environment, it is often necessary to adjust the parameters of the CRF online to maintain the same level of accuracy under changes no predicted during the training phase. Online parameter learning can be challenging since ground truth information is not available for newly encountered scenes. To address this issue, this paper proposes a stochastic gradient descent (SGD) method to learn the parameters of a constrained CRF (cCRF) in an online fashion. By leveraging the information from laser scans and image data the complexity of the labelling problem can be significantly reduced. The parameters are estimated by optimising a novel loss function that takes into account highly confident labels as a reference while eliminating the need for manual labelling. These labels are obtained purely based on the information from camera and laser sensors, in a self-supervised manner. Sensor data is pre-processed using methods such as convolutional nets, discriminant analysis, and Euclidean distance based clustering to extract reference labels. We show that this online parameter learning is robust to changes in the data distribution by selecting the learning rate appropriately. Experimental results are presented on the KITTI data set demonstrating the benefits of online CRF training.