3,033 research outputs found
A Proposal for Semantic Map Representation and Evaluation
Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset
Transfer Learning-Based Crack Detection by Autonomous UAVs
Unmanned Aerial Vehicles (UAVs) have recently shown great performance
collecting visual data through autonomous exploration and mapping in building
inspection. Yet, the number of studies is limited considering the post
processing of the data and its integration with autonomous UAVs. These will
enable huge steps onward into full automation of building inspection. In this
regard, this work presents a decision making tool for revisiting tasks in
visual building inspection by autonomous UAVs. The tool is an implementation of
fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack
detection. It offers an optional mechanism for task planning of revisiting
pinpoint locations during inspection. It is integrated to a quadrotor UAV
system that can autonomously navigate in GPS-denied environments. The UAV is
equipped with onboard sensors and computers for autonomous localization,
mapping and motion planning. The integrated system is tested through
simulations and real-world experiments. The results show that the system
achieves crack detection and autonomous navigation in GPS-denied environments
for building inspection
Fast and Robust Detection of Fallen People from a Mobile Robot
This paper deals with the problem of detecting fallen people lying on the
floor by means of a mobile robot equipped with a 3D depth sensor. In the
proposed algorithm, inspired by semantic segmentation techniques, the 3D scene
is over-segmented into small patches. Fallen people are then detected by means
of two SVM classifiers: the first one labels each patch, while the second one
captures the spatial relations between them. This novel approach showed to be
robust and fast. Indeed, thanks to the use of small patches, fallen people in
real cluttered scenes with objects side by side are correctly detected.
Moreover, the algorithm can be executed on a mobile robot fitted with a
standard laptop making it possible to exploit the 2D environmental map built by
the robot and the multiple points of view obtained during the robot navigation.
Additionally, this algorithm is robust to illumination changes since it does
not rely on RGB data but on depth data. All the methods have been thoroughly
validated on the IASLAB-RGBD Fallen Person Dataset, which is published online
as a further contribution. It consists of several static and dynamic sequences
with 15 different people and 2 different environments
Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality
We address the problem of interactively controlling the workspace of a mobile
robot to ensure a human-aware navigation. This is especially of relevance for
non-expert users living in human-robot shared spaces, e.g. home environments,
since they want to keep the control of their mobile robots, such as vacuum
cleaning or companion robots. Therefore, we introduce virtual borders that are
respected by a robot while performing its tasks. For this purpose, we employ a
RGB-D Google Tango tablet as human-robot interface in combination with an
augmented reality application to flexibly define virtual borders. We evaluated
our system with 15 non-expert users concerning accuracy, teaching time and
correctness and compared the results with other baseline methods based on
visual markers and a laser pointer. The experimental results show that our
method features an equally high accuracy while reducing the teaching time
significantly compared to the baseline methods. This holds for different border
lengths, shapes and variations in the teaching process. Finally, we
demonstrated the correctness of the approach, i.e. the mobile robot changes its
navigational behavior according to the user-defined virtual borders.Comment: Accepted on 2018 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), supplementary video: https://youtu.be/oQO8sQ0JBR
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd
International Conference on Interactive Collaborative Robotics (ICR 2018)
SkiMap: An Efficient Mapping Framework for Robot Navigation
We present a novel mapping framework for robot navigation which features a
multi-level querying system capable to obtain rapidly representations as
diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These
are inherently embedded into a memory and time efficient core data structure
organized as a Tree of SkipLists. Compared to the well-known Octree
representation, our approach exhibits a better time efficiency, thanks to its
simple and highly parallelizable computational structure, and a similar memory
footprint when mapping large workspaces. Peculiarly within the realm of mapping
for robot navigation, our framework supports realtime erosion and
re-integration of measurements upon reception of optimized poses from the
sensor tracker, so as to improve continuously the accuracy of the map.Comment: Accepted by International Conference on Robotics and Automation
(ICRA) 2017. This is the submitted version. The final published version may
be slightly differen
- …