99 research outputs found
Image-Based Localization Using Deep Neural Networks
Image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. It is a key component of many computer vision applications such as navigating autonomous vehicles and mobile robotics, simultaneous localization and mapping (SLAM), and augmented reality.
Currently, there are plenty of image-based localization methods proposed in the literature. Most state-of-the-art approaches are based on hand-crafted local features, such as SIFT, ORB, or SURF, and efficient 2D-to-3D matching using a 3D model. However, the limitations of the hand-crafted feature detector and descriptor become the bottleneck of these approaches. Recently, some promising deep neural network based localization approaches have been proposed. These approaches directly formulate 6 DoF pose estimation as a regression problem or use neural networks for generating 2D-3D correspondences, and thus no feature extraction or feature matching processes are required.
In this thesis, we first review two state-of-the-art approaches for image-based localization. The first approach is conventional hand-crafted local feature based (Active Search) and the second one is novel deep neural network based (DSAC). Building on the idea of DSAC, we then examine the use of conventional RANSAC and introduce a novel full-frame Coordinate CNN. We evaluate these methods on the 7-Scenes dataset of Microsoft Research, and extensive comparisons are made. The results show that our modifications to the original DSAC pipeline lead to better performance than the two state-of-the-art approaches
Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade
Camera pose estimation is an important problem in computer vision. Common
techniques either match the current image against keyframes with known poses,
directly regress the pose, or establish correspondences between keypoints in
the image and points in the scene to estimate the pose. In recent years,
regression forests have become a popular alternative to establish such
correspondences. They achieve accurate results, but have traditionally needed
to be trained offline on the target scene, preventing relocalisation in new
environments. Recently, we showed how to circumvent this limitation by adapting
a pre-trained forest to a new scene on the fly. The adapted forests achieved
relocalisation performance that was on par with that of offline forests, and
our approach was able to estimate the camera pose in close to real time. In
this paper, we present an extension of this work that achieves significantly
better relocalisation performance whilst running fully in real time. To achieve
this, we make several changes to the original approach: (i) instead of
accepting the camera pose hypothesis without question, we make it possible to
score the final few hypotheses using a geometric approach and select the most
promising; (ii) we chain several instantiations of our relocaliser together in
a cascade, allowing us to try faster but less accurate relocalisation first,
only falling back to slower, more accurate relocalisation as necessary; and
(iii) we tune the parameters of our cascade to achieve effective overall
performance. These changes allow us to significantly improve upon the
performance our original state-of-the-art method was able to achieve on the
well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional
contributions, we present a way of visualising the internal behaviour of our
forests and show how to entirely circumvent the need to pre-train a forest on a
generic scene.Comment: Tommaso Cavallari, Stuart Golodetz, Nicholas Lord and Julien Valentin
assert joint first authorshi
Geometry-Aware Learning of Maps for Camera Localization
Maps are a key component in image-based camera localization and visual SLAM
systems: they are used to establish geometric constraints between images,
correct drift in relative pose estimation, and relocalize cameras after lost
tracking. The exact definitions of maps, however, are often
application-specific and hand-crafted for different scenarios (e.g. 3D
landmarks, lines, planes, bags of visual words). We propose to represent maps
as a deep neural net called MapNet, which enables learning a data-driven map
representation. Unlike prior work on learning maps, MapNet exploits cheap and
ubiquitous sensory inputs like visual odometry and GPS in addition to images
and fuses them together for camera localization. Geometric constraints
expressed by these inputs, which have traditionally been used in bundle
adjustment or pose-graph optimization, are formulated as loss terms in MapNet
training and also used during inference. In addition to directly improving
localization accuracy, this allows us to update the MapNet (i.e., maps) in a
self-supervised manner using additional unlabeled video sequences from the
scene. We also propose a novel parameterization for camera rotation which is
better suited for deep-learning based camera pose regression. Experimental
results on both the indoor 7-Scenes dataset and the outdoor Oxford RobotCar
dataset show significant performance improvement over prior work. The MapNet
project webpage is https://goo.gl/mRB3Au.Comment: CVPR 2018 camera ready paper + supplementary materia
Visual Perception for Manipulation and Imitation in Humanoid Robots
This thesis deals with visual perception for manipulation and imitation in humanoid robots. In particular, real-time applicable methods for object recognition and pose estimation as well as for markerless human motion capture have been developed. As only sensor a small baseline stereo camera system (approx. human eye distance) was used. An extensive experimental evaluation has been performed on simulated as well as real image data from real-world scenarios using the humanoid robot ARMAR-III
- …