74,508 research outputs found
Featureless visual processing for SLAM in changing outdoor environments
Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features
Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Localization is a key requirement for mobile robot autonomy and human-robot
interaction. Vision-based localization is accurate and flexible, however, it
incurs a high computational burden which limits its application on many
resource-constrained platforms. In this paper, we address the problem of
performing real-time localization in large-scale 3D point cloud maps of
ever-growing size. While most systems using multi-modal information reduce
localization time by employing side-channel information in a coarse manner (eg.
WiFi for a rough prior position estimate), we propose to inter-weave the map
with rich sensory data. This multi-modal approach achieves two key goals
simultaneously. First, it enables us to harness additional sensory data to
localise against a map covering a vast area in real-time; and secondly, it also
allows us to roughly localise devices which are not equipped with a camera. The
key to our approach is a localization policy based on a sequential Monte Carlo
estimator. The localiser uses this policy to attempt point-matching only in
nodes where it is likely to succeed, significantly increasing the efficiency of
the localization process. The proposed multi-modal localization system is
evaluated extensively in a large museum building. The results show that our
multi-modal approach not only increases the localization accuracy but
significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
Deep Drone Racing: From Simulation to Reality with Domain Randomization
Dynamically changing environments, unreliable state estimation, and operation
under severe resource constraints are fundamental challenges that limit the
deployment of small autonomous drones. We address these challenges in the
context of autonomous, vision-based drone racing in dynamic environments. A
racing drone must traverse a track with possibly moving gates at high speed. We
enable this functionality by combining the performance of a state-of-the-art
planning and control system with the perceptual awareness of a convolutional
neural network (CNN). The resulting modular system is both platform- and
domain-independent: it is trained in simulation and deployed on a physical
quadrotor without any fine-tuning. The abundance of simulated data, generated
via domain randomization, makes our system robust to changes of illumination
and gate appearance. To the best of our knowledge, our approach is the first to
demonstrate zero-shot sim-to-real transfer on the task of agile drone flight.
We extensively test the precision and robustness of our system, both in
simulation and on a physical platform, and show significant improvements over
the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics
Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854
Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal
Model-free reinforcement learning has recently been shown to be effective at
learning navigation policies from complex image input. However, these
algorithms tend to require large amounts of interaction with the environment,
which can be prohibitively costly to obtain on robots in the real world. We
present an approach for efficiently learning goal-directed navigation policies
on a mobile robot, from only a single coverage traversal of recorded data. The
navigation agent learns an effective policy over a diverse action space in a
large heterogeneous environment consisting of more than 2km of travel, through
buildings and outdoor regions that collectively exhibit large variations in
visual appearance, self-similarity, and connectivity. We compare pretrained
visual encoders that enable precomputation of visual embeddings to achieve a
throughput of tens of thousands of transitions per second at training time on a
commodity desktop computer, allowing agents to learn from millions of
trajectories of experience in a matter of hours. We propose multiple forms of
computationally efficient stochastic augmentation to enable the learned policy
to generalise beyond these precomputed embeddings, and demonstrate successful
deployment of the learned policy on the real robot without fine tuning, despite
environmental appearance differences at test time. The dataset and code
required to reproduce these results and apply the technique to other datasets
and robots is made publicly available at rl-navigation.github.io/deployable
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
An adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In previous work we introduced a method to update the reference views in a topological map so that a mobile robot could continue to localize itself in a changing environment using omni-directional vision. In this work we extend this longterm updating mechanism to incorporate a spherical metric representation of the observed visual features for each node in the topological map. Using multi-view geometry we are then able to estimate the heading of the robot, in order to enable navigation between the nodes of the map, and to simultaneously adapt the spherical view representation in response to environmental changes. The results demonstrate the persistent performance of the proposed system in a long-term experiment
Long-term experiments with an adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability
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