477 research outputs found
Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach
Despite the existence of different error metrics for trajectory evaluation in SLAM, their theoretical justifications and connections are rarely studied, and few methods handle temporal association properly. In this work, we propose to formulate the trajectory evaluation problem in a probabilistic, continuous-time framework. By modeling the groundtruth as random variables, the concepts of absolute and relative error are generalized to be likelihood. Moreover, the groundtruth is represented as a piecewise Gaussian Process in continuous-time. Within this framework, we are able to establish theoretical connections between relative and absolute error metrics and handle temporal association in a principled manner
Rethinking Trajectory Evaluation for SLAM: a Probabilistic, Continuous-Time Approach
Despite the existence of different error metrics for trajectory evaluation in
SLAM, their theoretical justifications and connections are rarely studied, and
few methods handle temporal association properly. In this work, we propose to
formulate the trajectory evaluation problem in a probabilistic, continuous-time
framework. By modeling the groundtruth as random variables, the concepts of
absolute and relative error are generalized to be likelihood. Moreover, the
groundtruth is represented as a piecewise Gaussian Process in continuous-time.
Within this framework, we are able to establish theoretical connections between
relative and absolute error metrics and handle temporal association in a
principled manner.Comment: Accepted at ICRA19 Workshop on Dataset Generation and Benchmarking of
SLAM Algorithms for Robotics and VR/AR. Best paper awar
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments
One of the main open challenges in visual odometry (VO) is the robustness to
difficult illumination conditions or high dynamic range (HDR) environments. The
main difficulties in these situations come from both the limitations of the
sensors and the inability to perform a successful tracking of interest points
because of the bold assumptions in VO, such as brightness constancy. We address
this problem from a deep learning perspective, for which we first fine-tune a
Deep Neural Network (DNN) with the purpose of obtaining enhanced
representations of the sequences for VO. Then, we demonstrate how the insertion
of Long Short Term Memory (LSTM) allows us to obtain temporally consistent
sequences, as the estimation depends on previous states. However, the use of
very deep networks does not allow the insertion into a real-time VO framework;
therefore, we also propose a Convolutional Neural Network (CNN) of reduced size
capable of performing faster. Finally, we validate the enhanced representations
by evaluating the sequences produced by the two architectures in several
state-of-art VO algorithms, such as ORB-SLAM and DSO
Faster-than-Nyquist Signaling for MIMO Communications
Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission
technique, which has the potential to provide significant spectral efficiency
improvement. This paper studies the capacity of FTN signaling for both
frequency-flat and for frequency-selective multiple-input multiple-output
(MIMO) channels. We show that precoding in time and waterfilling in space is
capacity achieving for frequency-flat MIMO FTN. For frequency-selective fading,
joint waterfilling in time, space and frequency is required.Comment: Have been submitted to IEEE transactions on wireless communication
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