88 research outputs found
Visual SLAM for flying vehicles
The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments. © 2008 IEEE
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)
Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders
This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality
Experimental analysis of sample-based maps for long-term SLAM
This paper presents a system for long-term SLAM (simultaneous localization and mapping) by mobile service robots and its experimental evaluation in a real dynamic environment. To deal with the stability-plasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns), the environment is represented at multiple timescales simultaneously (5 in our experiments). A sample-based representation is
proposed, where older memories fade at different rates depending on the timescale, and robust statistics are used to interpret the samples. The dynamics of this representation are analysed in a five week experiment, measuring the relative influence of short- and long-term memories over time, and further demonstrating the robustness of the approach
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
[No abstract available
INFORMATION-DRIVEN AUTONOMOUS EXPLORATION FOR A VISION-BASED MAV
Most micro aerial vehicles (MAV) are flown manually by a pilot. When it comes to autonomous exploration for MAVs equipped with
cameras, we need a good exploration strategy for covering an unknown 3D environment in order to build an accurate map of the scene.
In particular, the robot must select appropriate viewpoints to acquire informative measurements. In this paper, we present an approach
that computes in real-time a smooth flight path with the exploration of a 3D environment using a vision-based MAV. We assume to
know a bounding box of the object or building to explore and our approach iteratively computes the next best viewpoints using a utility
function that considers the expected information gain of new measurements, the distance between viewpoints, and the smoothness of
the flight trajectories. In addition, the algorithm takes into account the elapsed time of the exploration run to safely land the MAV at
its starting point after a user specified time. We implemented our algorithm and our experiments suggest that it allows for a precise
reconstruction of the 3D environment while guiding the robot smoothly through the scene
Autonomous Robots manuscript No. (will be inserted by the editor) Learning Gas Distribution Models using Sparse Gaussian Process Mixtures
Abstract In this paper, we consider the problem of learning two-dimensional spatial models of gas distributions. To build models of gas distributions that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We formulate this task as a regression problem. To deal with the specific properties of gas distributions, we propose a sparse Gaussian process mixture model, which allows us to accurately represent the smooth background signal and the areas with patches of high concentrations. We furthermore integrate the sparsification of the training data into an EM procedure that we apply for learning the mixture components and the gating function. Our approach has been implemented and tested using datasets recorded with a real mobile robot equipped with an electronic nose. The experiments demonstrate that our technique is well-suited for predicting gas concentrations at new query locations and that it outperforms alternative and previously proposed methods in robotics
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