1,059 research outputs found
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
This paper presents a novel tightly-coupled keyframe-based Simultaneous
Localization and Mapping (SLAM) system with loop-closing and relocalization
capabilities targeted for the underwater domain. Our previous work, SVIn,
augmented the state-of-the-art visual-inertial state estimation package OKVIS
to accommodate acoustic data from sonar in a non-linear optimization-based
framework. This paper addresses drift and loss of localization -- one of the
main problems affecting other packages in underwater domain -- by providing the
following main contributions: a robust initialization method to refine scale
using depth measurements, a fast preprocessing step to enhance the image
quality, and a real-time loop-closing and relocalization method using bag of
words (BoW). An additional contribution is the addition of depth measurements
from a pressure sensor to the tightly-coupled optimization formulation.
Experimental results on datasets collected with a custom-made underwater sensor
suite and an autonomous underwater vehicle from challenging underwater
environments with poor visibility demonstrate performance never achieved before
in terms of accuracy and robustness
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regularize the ICP registration technique. We also present a
technique to filter the pairs of 3D matched points based on the distribution of
their distances. This filter is implemented efficiently on the GPU. Estimating
the distribution of the distances helps control the number of iterations
necessary for the convergence of the ICP algorithm. Finally, we show
experimental results that highlight improvements in robustness, a speed-up of
almost 12%, and a gain in tracking quality of 53% for the ATE metric on the
Freiburg benchmark.Comment: CVPR Workshop on Visual Odometry and Computer Vision Applications
Based on Location Clues 201
Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models
Perceptual aliasing is one of the main causes of failure for Simultaneous
Localization and Mapping (SLAM) systems operating in the wild. Perceptual
aliasing is the phenomenon where different places generate a similar visual
(or, in general, perceptual) footprint. This causes spurious measurements to be
fed to the SLAM estimator, which typically results in incorrect localization
and mapping results. The problem is exacerbated by the fact that those outliers
are highly correlated, in the sense that perceptual aliasing creates a large
number of mutually-consistent outliers. Another issue stems from the fact that
most state-of-the-art techniques rely on a given trajectory guess (e.g., from
odometry) to discern between inliers and outliers and this makes the resulting
pipeline brittle, since the accumulation of error may result in incorrect
choices and recovery from failures is far from trivial. This work provides a
unified framework to model perceptual aliasing in SLAM and provides practical
algorithms that can cope with outliers without relying on any initial guess. We
present two main contributions. The first is a Discrete-Continuous Graphical
Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the
standard SLAM problem, while the discrete portion describes the selection of
the outliers and models their correlation. The second contribution is a
semidefinite relaxation to perform inference in the DC-GM that returns
estimates with provable sub-optimality guarantees. Experimental results on
standard benchmarking datasets show that the proposed technique compares
favorably with state-of-the-art methods while not relying on an initial guess
for optimization.Comment: 13 pages, 14 figures, 1 tabl
Learning a Bias Correction for Lidar-only Motion Estimation
This paper presents a novel technique to correct for bias in a classical
estimator using a learning approach. We apply a learned bias correction to a
lidar-only motion estimation pipeline. Our technique trains a Gaussian process
(GP) regression model using data with ground truth. The inputs to the model are
high-level features derived from the geometry of the point-clouds, and the
outputs are the predicted biases between poses computed by the estimator and
the ground truth. The predicted biases are applied as a correction to the poses
computed by the estimator.
Our technique is evaluated on over 50km of lidar data, which includes the
KITTI odometry benchmark and lidar datasets collected around the University of
Toronto campus. After applying the learned bias correction, we obtained
significant improvements to lidar odometry in all datasets tested. We achieved
around 10% reduction in errors on all datasets from an already accurate lidar
odometry algorithm, at the expense of only less than 1% increase in
computational cost at run-time.Comment: 15th Conference on Computer and Robot Vision (CRV 2018
Comparative Evaluation of RGB-D SLAM Methods for Humanoid Robot Localization and Mapping
In this paper, we conducted a comparative evaluation of three RGB-D SLAM
(Simultaneous Localization and Mapping) algorithms: RTAB-Map, ORB-SLAM3, and
OpenVSLAM for SURENA-V humanoid robot localization and mapping. Our test
involves the robot to follow a full circular pattern, with an Intel RealSense
D435 RGB-D camera installed on its head. In assessing localization accuracy,
ORB-SLAM3 outperformed the others with an ATE of 0.1073, followed by RTAB-Map
at 0.1641 and OpenVSLAM at 0.1847. However, it should be noted that both
ORB-SLAM3 and OpenVSLAM faced challenges in maintaining accurate odometry when
the robot encountered a wall with limited feature points. Nevertheless,
OpenVSLAM demonstrated the ability to detect loop closures and successfully
relocalize itself within the map when the robot approached its initial
location. The investigation also extended to mapping capabilities, where
RTAB-Map excelled by offering diverse mapping outputs, including dense,
OctoMap, and occupancy grid maps. In contrast, both ORB-SLAM3 and OpenVSLAM
provided only sparse maps.Comment: 6 pages, 11th RSI International Conference on Robotics and
Mechatronics (ICRoM 2023
A Comparison of Monocular Visual SLAM and Visual Odometry Methods Applied to 3D Reconstruction
This work was supported by the SDAS Research Group (www.sdas-group.com accessed
on 16 June 2023).Pure monocular 3D reconstruction is a complex problem that has attracted the research community's interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera's ego-motion; so, many methods have been proposed. However, most of these methods have not been evaluated on large datasets and under various motion patterns, have not been tested under the same metrics, and most of them have not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine-learning-based methods, and two updates of the best methods to identify the advantages and limitations of each category of the taxonomy and test whether the addition of machine learning or updates on those methods improved them significantly. Thus, we evaluated each algorithm using the TUM-Mono dataset and benchmark, and we performed an inferential statistical analysis to identify the significant differences through its metrics. The results determined that the sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly enhanced the geometric-based methods' performance from different perspectives.SDAS Research Grou
Evaluation of RGB-D SLAM in Large Indoor Environments
Simultaneous localization and mapping (SLAM) is one of the key components of
a control system that aims to ensure autonomous navigation of a mobile robot in
unknown environments. In a variety of practical cases a robot might need to
travel long distances in order to accomplish its mission. This requires
long-term work of SLAM methods and building large maps. Consequently the
computational burden (including high memory consumption for map storage)
becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific
techniques and optimizations to tackle this challenge, still their performance
in long-term scenarios needs proper assessment. To this end, we perform an
empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods,
suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them
in a large simulated indoor environment, consisting of corridors and halls,
while varying the odometer noise for a more realistic setup. We provide both
qualitative and quantitative analysis of both methods uncovering their
strengths and weaknesses. We find that both methods build a high-quality map
with low odometry noise but tend to fail with high odometry noise. Voxgraph has
lower relative trajectory estimation error and memory consumption than
RTAB-Map, while its absolute error is higher.Comment: This is a pre-print of the paper accepted to ICR 2022 conferenc
A Comparative Study of Registration Methods for RGB-D Video of Static Scenes
The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.This work has been supported by a grant from the Spanish Government, DPI2013-40534-R, University of Alicante projects GRE11-01 and a grant from the Valencian Government, GV/2013/005
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