48,381 research outputs found
PanopticNDT: Efficient and Robust Panoptic Mapping
As the application scenarios of mobile robots are getting more complex and
challenging, scene understanding becomes increasingly crucial. A mobile robot
that is supposed to operate autonomously in indoor environments must have
precise knowledge about what objects are present, where they are, what their
spatial extent is, and how they can be reached; i.e., information about free
space is also crucial. Panoptic mapping is a powerful instrument providing such
information. However, building 3D panoptic maps with high spatial resolution is
challenging on mobile robots, given their limited computing capabilities. In
this paper, we propose PanopticNDT - an efficient and robust panoptic mapping
approach based on occupancy normal distribution transform (NDT) mapping. We
evaluate our approach on the publicly available datasets Hypersim and
ScanNetV2. The results reveal that our approach can represent panoptic
information at a higher level of detail than other state-of-the-art approaches
while enabling real-time panoptic mapping on mobile robots. Finally, we prove
the real-world applicability of PanopticNDT with qualitative results in a
domestic application.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), 202
Deep Network Uncertainty Maps for Indoor Navigation
Most mobile robots for indoor use rely on 2D laser scanners for localization,
mapping and navigation. These sensors, however, cannot detect transparent
surfaces or measure the full occupancy of complex objects such as tables. Deep
Neural Networks have recently been proposed to overcome this limitation by
learning to estimate object occupancy. These estimates are nevertheless subject
to uncertainty, making the evaluation of their confidence an important issue
for these measures to be useful for autonomous navigation and mapping. In this
work we approach the problem from two sides. First we discuss uncertainty
estimation in deep models, proposing a solution based on a fully convolutional
neural network. The proposed architecture is not restricted by the assumption
that the uncertainty follows a Gaussian model, as in the case of many popular
solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout.
We present results showing that uncertainty over obstacle distances is actually
better modeled with a Laplace distribution. Then, we propose a novel approach
to build maps based on Deep Neural Network uncertainty models. In particular,
we present an algorithm to build a map that includes information over obstacle
distance estimates while taking into account the level of uncertainty in each
estimate. We show how the constructed map can be used to increase global
navigation safety by planning trajectories which avoid areas of high
uncertainty, enabling higher autonomy for mobile robots in indoor settings.Comment: Accepted for publication in "2019 IEEE-RAS International Conference
on Humanoid Robots (Humanoids)
Airborne chemical sensing with mobile robots
Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations
STAM: A System of Tracking and Mapping in Real Environments
We have implemented a system of tracking mobile robots and mapping an unstructured environment, using up to 25 wireless sensor nodes in an indoor setting. These sensor nodes form an ad hoc network of beacons, self-localize with respect to three anchor nodes, and then track the locations of mobile robots in the field. The system described here was motivated by search and rescue applications, and has been demonstrated in real physical environments
Robots for Exploration, Digital Preservation and Visualization of Archeological Sites
Monitoring and conservation of archaeological sites
are important activities necessary to prevent damage or to
perform restoration on cultural heritage. Standard techniques,
like mapping and digitizing, are typically used to document the
status of such sites. While these task are normally accomplished
manually by humans, this is not possible when dealing with
hard-to-access areas. For example, due to the possibility of
structural collapses, underground tunnels like catacombs are
considered highly unstable environments. Moreover, they are full
of radioactive gas radon that limits the presence of people only
for few minutes. The progress recently made in the artificial
intelligence and robotics field opened new possibilities for mobile
robots to be used in locations where humans are not allowed
to enter. The ROVINA project aims at developing autonomous
mobile robots to make faster, cheaper and safer the monitoring of
archaeological sites. ROVINA will be evaluated on the catacombs
of Priscilla (in Rome) and S. Gennaro (in Naples)
Robust Environmental Mapping by Mobile Sensor Networks
Constructing a spatial map of environmental parameters is a crucial step to
preventing hazardous chemical leakages, forest fires, or while estimating a
spatially distributed physical quantities such as terrain elevation. Although
prior methods can do such mapping tasks efficiently via dispatching a group of
autonomous agents, they are unable to ensure satisfactory convergence to the
underlying ground truth distribution in a decentralized manner when any of the
agents fail. Since the types of agents utilized to perform such mapping are
typically inexpensive and prone to failure, this results in poor overall
mapping performance in real-world applications, which can in certain cases
endanger human safety. This paper presents a Bayesian approach for robust
spatial mapping of environmental parameters by deploying a group of mobile
robots capable of ad-hoc communication equipped with short-range sensors in the
presence of hardware failures. Our approach first utilizes a variant of the
Voronoi diagram to partition the region to be mapped into disjoint regions that
are each associated with at least one robot. These robots are then deployed in
a decentralized manner to maximize the likelihood that at least one robot
detects every target in their associated region despite a non-zero probability
of failure. A suite of simulation results is presented to demonstrate the
effectiveness and robustness of the proposed method when compared to existing
techniques.Comment: accepted to icra 201
- …