Multi-Modal Learning For Adaptive Scene Understanding

Modern robotics systems typically possess sensors of different modalities. Segmenting scenes observed by the robot into a discrete set of classes is a central requirement for autonomy. Equally, when a robot navigates through an unknown environment, it is often necessary to adjust the parameters of the scene segmentation model to maintain the same level of accuracy in changing situations. This thesis explores efficient means of adaptive semantic scene segmentation in an online setting with the use of multiple sensor modalities. First, we devise a novel conditional random field(CRF) inference method for scene segmentation that incorporates global constraints, enforcing particular sets of nodes to be assigned the same class label. To do this efficiently, the CRF is formulated as a relaxed quadratic program whose maximum a posteriori(MAP) solution is found using a gradient-based optimization approach. These global constraints are useful, since they can encode "a priori" information about the final labeling. This new formulation also reduces the dimensionality of the original image-labeling problem. The proposed model is employed in an urban street scene understanding task. Camera data is used for the CRF based semantic segmentation while global constraints are derived from 3D laser point clouds. Second, an approach to learn CRF parameters without the need for manually labeled training data is proposed. The model parameters are estimated by optimizing a novel loss function using self supervised reference labels, obtained based on the information from camera and laser with minimum amount of human supervision. Third, an approach that can conduct the parameter optimization while increasing the model robustness to non-stationary data distributions in the long trajectories is proposed. We adopted stochastic gradient descent to achieve this goal by using a learning rate that can appropriately grow or diminish to gain adaptability to changes in the data distribution