  Deep Learning based techniques have been adopted with precision to solve a
lot of standard computer vision problems, some of which are image
classification, object detection and segmentation. Despite the widespread
success of these approaches, they have not yet been exploited largely for
solving the standard perception related problems encountered in autonomous
navigation such as Visual Odometry (VO), Structure from Motion (SfM) and
Simultaneous Localization and Mapping (SLAM). This paper analyzes the problem
of Monocular Visual Odometry using a Deep Learning-based framework, instead of
the regular 'feature detection and tracking' pipeline approaches. Several
experiments were performed to understand the influence of a known/unknown
environment, a conventional trackable feature and pre-trained activations tuned
for object classification on the network's ability to accurately estimate the
motion trajectory of the camera (or the vehicle). Based on these observations,
we propose a Convolutional Neural Network architecture, best suited for
estimating the object's pose under known environment conditions, and displays
promising results when it comes to inferring the actual scale using just a
single camera in real-time.
