A Deep Learning Approach for Robust Corridor Following For an autonomous corridor following task where the environment is continuously changing, several forms of environmental noise prevent an automated feature extraction procedure from performing reliably. Moreover, in cases where predefined features are absent from the captured data, a well defined control signal for performing the servoing task fails to get produced. In order to overcome these drawbacks, we present in this work, using a convolutional neural network (CNN) to directly estimate the required control signal from an image, encompassing feature extraction and control law computation into one single endtoend framework. In particular, we study the task of autonomous corridor following using a CNN and present clear advantages in cases where a traditional method used for performing the same task fails to give a reliable outcome. We evaluate the performance of our method on this task on a Wheelchair Platform developed at our institute for this purpose.

A Convolutional Neural Network Feature Detection Approach to Autonomous Quadrotor Indoor Navigation Object detection, extended to recognize and localize indoor structural features, is used to enable a quadrotor drone to autonomously navigate through indoor environments. The video stream from a monocular frontfacing camera onboard a quadrotor drone is fed to an offboard system that runs a Convolutional Neural Network (CNN) object detection algorithm to identify specific features such as deadends, doors, and intersections in hallways. Using pixelscale dimensions of the bounding boxes around the recognized objects, the distance to intersections, deadends and doorways can be estimated accurately using a Support Vector Regression (SVR) model to generate flight control commands for consistent realtime autonomous navigation at flight speeds approaching 2 ms.