|Title:||Invariant Landmark Detection for highly accurate positioning|
|Abstract:|| An essential task to enable highly autonomous driving is the self-localization and ego-motion estimation of the car. Together, they enable accurate absolute positioning and reasoning about the road ahead for e.g. path planning. If a single camera is used as sensor to measure the current vehicle location, positioning is based on visual landmarks and is related to the problem of visual odometry. Current navigation systems rely solely on GPS and vehicle odometry. Newer systems use object detections in the image, like traffic signs and road markings to triangulate the vehicle position within a map. To do so, visual landmarks are detected in the camera image and their position relative to the vehicle is computed. Given the landmark positions, the most likely position of the vehicle with respect to the landmarks in the map can be deduced. Current approaches use detected objects as landmarks (e.g. traffic signs/lights, poles, reflectors, lane markings), but often there are not enough of these objects to localize accurately. |
In the scope of this project, a method to detect more generic landmarks should be developed. This method, that will be focusing on deep learning, should extract features that are more invariant to different invariances (e.g. illumination) and also provide a good matchability.
• Create a dataset from different sources: already existing datasets, public webcam streams and synthetic datasets.
• Design and implement a method/network to extract robust generic landmarks/features in different environments (highway, city, country roads) and match them to previously extracted landmarks.
• Leverage deep learning in order to achieve a high invariance to different environment conditions.
• Literature review of methods to extract robust landmarks with focus on:
o Robustness to changes in appearance, viewpoint.
o Uniqueness to match them corretly to already extracted landmarks.
• Implementation and evaluation of a deep neural network that is capable of extracting invariant features, that offer the possibility for robust matching.
• Application of the feature in a state-of-the-art SLAM algorithm.
 LIFT: https://arxiv.org/abs/1603.09114
 TILDE: https://infoscience.epfl.ch/record/206786/files/top.pdf
 Playing for Data: https://download.visinf.tu-darmstadt.de/data/from_games/data/eccv-2016-richter-playing_for_data.pdf
 ORB_SLAM: http://webdiis.unizar.es/~raulmur/orbslam/
|Director:||Prof. Nassir Navab|
|Supervisor:||Jakob Mayr, Federico Tombari|
|Area:||Machine Learning, Computer Vision|