Learning-Based Vision and Its Application to Autonomous Indoor Navigation

Adaptation is critical to autonomous navigation of mobile robots. Many adaptive mechanisms have been implemented, ranging from simple color thresholding to complicated learning with artificial neural networks (ANN). The major focus of this thesis lies in machine learning for vision-based navigation. Two well known vision-based navigation systems are ALVINN and ROBIN developed by Carnegie-Mellon University and University of Maryland, respectively. ALVINN uses a two-layer feedforward neural network while ROBIN relies on a radial basis function network (RBFN). Although current ANN-based methods have achieved great success in vision-based navigation, they have two major disadvantages: (1) Local minimum problem: The training of either multilayer perceptron or radial basis function network can get stuck at poor local minimums. (2) The flexibility problem: After the system has been trained in certain r..