1,893 research outputs found
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
Active depth cameras suffer from several limitations, which cause incomplete
and noisy depth maps, and may consequently affect the performance of RGB-D
Odometry. To address this issue, this paper presents a visual odometry method
based on point and line features that leverages both measurements from a depth
sensor and depth estimates from camera motion. Depth estimates are generated
continuously by a probabilistic depth estimation framework for both types of
features to compensate for the lack of depth measurements and inaccurate
feature depth associations. The framework models explicitly the uncertainty of
triangulating depth from both point and line observations to validate and
obtain precise estimates. Furthermore, depth measurements are exploited by
propagating them through a depth map registration module and using a
frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D
reprojection errors, independently. Results on RGB-D sequences captured on
large indoor and outdoor scenes, where depth sensor limitations are critical,
show that the combination of depth measurements and estimates through our
approach is able to overcome the absence and inaccuracy of depth measurements.Comment: IROS 201
A Robust Localization System for Inspection Robots in Sewer Networks â€
Sewers represent a very important infrastructure of cities whose state should be monitored
periodically. However, the length of such infrastructure prevents sensor networks from being
applicable. In this paper, we present a mobile platform (SIAR) designed to inspect the sewer network.
It is capable of sensing gas concentrations and detecting failures in the network such as cracks and
holes in the floor and walls or zones were the water is not flowing. These alarms should be precisely
geo-localized to allow the operators performing the required correcting measures. To this end, this
paper presents a robust localization system for global pose estimation on sewers. It makes use of prior
information of the sewer network, including its topology, the different cross sections traversed and
the position of some elements such as manholes. The system is based on a Monte Carlo Localization
system that fuses wheel and RGB-D odometry for the prediction stage. The update step takes into
account the sewer network topology for discarding wrong hypotheses. Additionally, the localization
is further refined with novel updating steps proposed in this paper which are activated whenever
a discrete element in the sewer network is detected or the relative orientation of the robot over the
sewer gallery could be estimated. Each part of the system has been validated with real data obtained
from the sewers of Barcelona. The whole system is able to obtain median localization errors in the
order of one meter in all cases. Finally, the paper also includes comparisons with state-of-the-art
Simultaneous Localization and Mapping (SLAM) systems that demonstrate the convenience of the
approach.Unión Europea ECHORD ++ 601116Ministerio de Ciencia, Innovación y Universidades de España RTI2018-100847-B-C2
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