122,090 research outputs found
SemanticLoop: loop closure with 3D semantic graph matching
Loop closure can effectively correct the accumulated error in robot
localization, which plays a critical role in the long-term navigation of the
robot. Traditional appearance-based methods rely on local features and are
prone to failure in ambiguous environments. On the other hand, object
recognition can infer objects' category, pose, and extent. These objects can
serve as stable semantic landmarks for viewpoint-independent and non-ambiguous
loop closure. However, there is a critical object-level data association
problem due to the lack of efficient and robust algorithms.
We introduce a novel object-level data association algorithm, which
incorporates IoU, instance-level embedding, and detection uncertainty,
formulated as a linear assignment problem. Then, we model the objects as TSDF
volumes and represent the environment as a 3D graph with semantics and
topology. Next, we propose a graph matching-based loop detection based on the
reconstructed 3D semantic graphs and correct the accumulated error by aligning
the matched objects. Finally, we refine the object poses and camera trajectory
in an object-level pose graph optimization.
Experimental results show that the proposed object-level data association
method significantly outperforms the commonly used nearest-neighbor method in
accuracy. Our graph matching-based loop closure is more robust to environmental
appearance changes than existing appearance-based methods
Spin Glass Models of Markov Random Fields
This paper presents a novel algorithm for robust object recognition. We propose to model the visual appearance of objects via probability density functions. The algorithm consists of a fully connected Markov random field with energy function derived from results of statistical physics of spin glasses. Markov random fields and spin glass energy functions are combined together via nonlinear kernel functions; we call the model Spin Glass\--Markov Random Field. Full connectivity enables to take into account the global appearance of the object, and its specific local characteristics at the same time, resulting in robustness to noise, occlusions and cluttered background. We show with theoretical analysis and experiments that this new model is competitive with state-of-the-art algorithms
Object matching using boundary descriptors
The problem of object recognition is of immense practical importance and potential, and the last decade has witnessed a number of breakthroughs in the state of the art. Most of the past object recognition work focuses on textured objects and local appearance descriptors extracted around salient points in an image. These methods fail in the matching of smooth, untextured objects for which salient point detection does not produce robust results. The recently proposed bag of boundaries (BoB) method is the first to directly address this problem. Since the texture of smooth objects is largely uninformative, BoB focuses on describing and matching objects based on their post-segmentation boundaries. Herein we address three major weaknesses of this work. The first of these is the uniform treatment of all boundary segments. Instead, we describe a method for detecting the locations and scales of salient boundary segments. Secondly, while the BoB method uses an image based elementary descriptor (HoGs + occupancy matrix), we propose a more compact descriptor based on the local profile of boundary normals’ directions. Lastly, we conduct a far more systematic evaluation, both of the bag of boundaries method and the method proposed here. Using a large public database, we demonstrate that our method exhibits greater robustness while at the same time achieving a major computational saving – object representation is extracted from an image in only 6% of the time needed to extract a bag of boundaries, and the storage requirement is similarly reduced to less than 8%
Automatic vehicle tracking and recognition from aerial image sequences
This paper addresses the problem of automated vehicle tracking and
recognition from aerial image sequences. Motivated by its successes in the
existing literature focus on the use of linear appearance subspaces to describe
multi-view object appearance and highlight the challenges involved in their
application as a part of a practical system. A working solution which includes
steps for data extraction and normalization is described. In experiments on
real-world data the proposed methodology achieved promising results with a high
correct recognition rate and few, meaningful errors (type II errors whereby
genuinely similar targets are sometimes being confused with one another).
Directions for future research and possible improvements of the proposed method
are discussed
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