Name
Abstract | ||
|---|---|---|
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 u0027feature detection and trackingu0027 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 networku0027s 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 objectu0027s 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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Structure from motion,Visual odometry,Computer science,Convolutional neural network,Artificial intelligence,Deep learning,Monocular,Simultaneous localization and mapping,Contextual image classification,Computer vision,Object detection,Pattern recognition,Machine learning |
DocType | Volume | Citations |
Journal | abs/1611.06069 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vikram Mohanty | 1 | 1 | 0.69 |
| Shubh Agrawal | 2 | 1 | 0.69 |
| Shaswat Datta | 3 | 1 | 0.35 |
| Arna Ghosh | 4 | 4 | 2.78 |
| Vishnu Dutt Sharma | 5 | 1 | 0.69 |
| Debashish Chakravarty | 6 | 1 | 0.35 |