4,218 research outputs found
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating
the pose of vehicles without any prior knowledge. In this paper, a solution to
this problem is presented by achieving place recognition and metric pose
estimation in the global prior map. Specifically, we present a semi-handcrafted
representation learning method for LiDAR point clouds using siamese LocNets,
which states the place recognition problem to a similarity modeling problem.
With the final learned representations by LocNet, a global localization
framework with range-only observations is proposed. To demonstrate the
performance and effectiveness of our global localization system, KITTI dataset
is employed for comparison with other algorithms, and also on our long-time
multi-session datasets for evaluation. The result shows that our system can
achieve high accuracy.Comment: 6 pages, IV 2018 accepte
Semi-supervised Vector-Quantization in Visual SLAM using HGCN
In this paper, two semi-supervised appearance based loop closure detection
technique, HGCN-FABMAP and HGCN-BoW are introduced. Furthermore an extension to
the current state of the art localization SLAM algorithm, ORB-SLAM, is
presented. The proposed HGCN-FABMAP method is implemented in an off-line manner
incorporating Bayesian probabilistic schema for loop detection decision making.
Specifically, we let a Hyperbolic Graph Convolutional Neural Network (HGCN) to
operate over the SURF features graph space, and perform vector quantization
part of the SLAM procedure. This part previously was performed in an
unsupervised manner using algorithms like HKmeans, kmeans++,..etc. The main
Advantage of using HGCN, is that it scales linearly in number of graph edges.
Experimental results shows that HGCN-FABMAP algorithm needs far more cluster
centroids than HGCN-ORB, otherwise it fails to detect loop closures. Therefore
we consider HGCN-ORB to be more efficient in terms of memory consumption, also
we conclude the superiority of HGCN-BoW and HGCN-FABMAP with respect to other
algorithms
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
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