4,111 research outputs found
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
Size, weight, and power constrained platforms impose constraints on
computational resources that introduce unique challenges in implementing
localization algorithms. We present a framework to perform fast localization on
such platforms enabled by the compressive capabilities of Gaussian Mixture
Model representations of point cloud data. Given raw structural data from a
depth sensor and pitch and roll estimates from an on-board attitude reference
system, a multi-hypothesis particle filter localizes the vehicle by exploiting
the likelihood of the data originating from the mixture model. We demonstrate
analysis of this likelihood in the vicinity of the ground truth pose and detail
its utilization in a particle filter-based vehicle localization strategy, and
later present results of real-time implementations on a desktop system and an
off-the-shelf embedded platform that outperform localization results from
running a state-of-the-art algorithm on the same environment
A comparative evaluation of interest point detectors and local descriptors for visual SLAM
Abstract In this paper we compare the behavior of different interest points detectors and descriptors under the
conditions needed to be used as landmarks in vision-based simultaneous localization and mapping (SLAM).
We evaluate the repeatability of the detectors, as well as the invariance and distinctiveness of the descriptors,
under different perceptual conditions using sequences of images representing planar objects as well as 3D scenes.
We believe that this information will be useful when selecting an appropriat
Learning to Divide and Conquer for Online Multi-Target Tracking
Online Multiple Target Tracking (MTT) is often addressed within the
tracking-by-detection paradigm. Detections are previously extracted
independently in each frame and then objects trajectories are built by
maximizing specifically designed coherence functions. Nevertheless, ambiguities
arise in presence of occlusions or detection errors. In this paper we claim
that the ambiguities in tracking could be solved by a selective use of the
features, by working with more reliable features if possible and exploiting a
deeper representation of the target only if necessary. To this end, we propose
an online divide and conquer tracker for static camera scenes, which partitions
the assignment problem in local subproblems and solves them by selectively
choosing and combining the best features. The complete framework is cast as a
structural learning task that unifies these phases and learns tracker
parameters from examples. Experiments on two different datasets highlights a
significant improvement of tracking performances (MOTA +10%) over the state of
the art
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