401 research outputs found
StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments
We propose a novel visual-inertial odometry approach that adopts structural
regularity in man-made environments. Instead of using Manhattan world
assumption, we use Atlanta world model to describe such regularity. An Atlanta
world is a world that contains multiple local Manhattan worlds with different
heading directions. Each local Manhattan world is detected on-the-fly, and
their headings are gradually refined by the state estimator when new
observations are coming. With fully exploration of structural lines that
aligned with each local Manhattan worlds, our visual-inertial odometry method
become more accurate and robust, as well as much more flexible to different
kinds of complex man-made environments. Through extensive benchmark tests and
real-world tests, the results show that the proposed approach outperforms
existing visual-inertial systems in large-scale man-made environmentsComment: 15 pages,15 figure
Review and classification of vision-based localisation techniques in unknown environments
International audienceThis study presents a review of the state-of-the-art and a novel classification of current vision-based localisation techniques in unknown environments. Indeed, because of progresses made in computer vision, it is now possible to consider vision-based systems as promising navigation means that can complement traditional navigation sensors like global navigation satellite systems (GNSSs) and inertial navigation systems. This study aims to review techniques employing a camera as a localisation sensor, provide a classification of techniques and introduce schemes that exploit the use of video information within a multi-sensor system. In fact, a general model is needed to better compare existing techniques in order to decide which approach is appropriate and which are the innovation axes. In addition, existing classifications only consider techniques based on vision as a standalone tool and do not consider video as a sensor among others. The focus is addressed to scenarios where no a priori knowledge of the environment is provided. In fact, these scenarios are the most challenging since the system has to cope with objects as they appear in the scene without any prior information about their expected position
Visual-inertial self-calibration on informative motion segments
Environmental conditions and external effects, such as shocks, have a
significant impact on the calibration parameters of visual-inertial sensor
systems. Thus long-term operation of these systems cannot fully rely on factory
calibration. Since the observability of certain parameters is highly dependent
on the motion of the device, using short data segments at device initialization
may yield poor results. When such systems are additionally subject to energy
constraints, it is also infeasible to use full-batch approaches on a big
dataset and careful selection of the data is of high importance. In this paper,
we present a novel approach for resource efficient self-calibration of
visual-inertial sensor systems. This is achieved by casting the calibration as
a segment-based optimization problem that can be run on a small subset of
informative segments. Consequently, the computational burden is limited as only
a predefined number of segments is used. We also propose an efficient
information-theoretic selection to identify such informative motion segments.
In evaluations on a challenging dataset, we show our approach to significantly
outperform state-of-the-art in terms of computational burden while maintaining
a comparable accuracy
Attention and Anticipation in Fast Visual-Inertial Navigation
We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to
estimate its state using an on-board camera and an inertial sensor, without any
prior knowledge of the external environment. We consider the case in which the
robot can allocate limited resources to VIN, due to tight computational
constraints. Therefore, we answer the following question: under limited
resources, what are the most relevant visual cues to maximize the performance
of visual-inertial navigation? Our approach has four key ingredients. First, it
is task-driven, in that the selection of the visual cues is guided by a metric
quantifying the VIN performance. Second, it exploits the notion of
anticipation, since it uses a simplified model for forward-simulation of robot
dynamics, predicting the utility of a set of visual cues over a future time
horizon. Third, it is efficient and easy to implement, since it leads to a
greedy algorithm for the selection of the most relevant visual cues. Fourth, it
provides formal performance guarantees: we leverage submodularity to prove that
the greedy selection cannot be far from the optimal (combinatorial) selection.
Simulations and real experiments on agile drones show that our approach ensures
state-of-the-art VIN performance while maintaining a lean processing time. In
the easy scenarios, our approach outperforms appearance-based feature selection
in terms of localization errors. In the most challenging scenarios, it enables
accurate visual-inertial navigation while appearance-based feature selection
fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table
Four years of multi-modal odometry and mapping on the rail vehicles
Precise, seamless, and efficient train localization as well as long-term
railway environment monitoring is the essential property towards reliability,
availability, maintainability, and safety (RAMS) engineering for railroad
systems. Simultaneous localization and mapping (SLAM) is right at the core of
solving the two problems concurrently. In this end, we propose a
high-performance and versatile multi-modal framework in this paper, targeted
for the odometry and mapping task for various rail vehicles. Our system is
built atop an inertial-centric state estimator that tightly couples light
detection and ranging (LiDAR), visual, optionally satellite navigation and
map-based localization information with the convenience and extendibility of
loosely coupled methods. The inertial sensors IMU and wheel encoder are treated
as the primary sensor, which achieves the observations from subsystems to
constrain the accelerometer and gyroscope biases. Compared to point-only
LiDAR-inertial methods, our approach leverages more geometry information by
introducing both track plane and electric power pillars into state estimation.
The Visual-inertial subsystem also utilizes the environmental structure
information by employing both lines and points. Besides, the method is capable
of handling sensor failures by automatic reconfiguration bypassing failure
modules. Our proposed method has been extensively tested in the long-during
railway environments over four years, including general-speed, high-speed and
metro, both passenger and freight traffic are investigated. Further, we aim to
share, in an open way, the experience, problems, and successes of our group
with the robotics community so that those that work in such environments can
avoid these errors. In this view, we open source some of the datasets to
benefit the research community
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