15,501 research outputs found
Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
Modern 3D laser-range scanners have a high data rate, making online
simultaneous localization and mapping (SLAM) computationally challenging.
Recursive state estimation techniques are efficient but commit to a state
estimate immediately after a new scan is made, which may lead to misalignments
of measurements. We present a 3D SLAM approach that allows for refining
alignments during online mapping. Our method is based on efficient local
mapping and a hierarchical optimization back-end. Measurements of a 3D laser
scanner are aggregated in local multiresolution maps by means of surfel-based
registration. The local maps are used in a multi-level graph for allocentric
mapping and localization. In order to incorporate corrections when refining the
alignment, the individual 3D scans in the local map are modeled as a sub-graph
and graph optimization is performed to account for drift and misalignments in
the local maps. Furthermore, in each sub-graph, a continuous-time
representation of the sensor trajectory allows to correct measurements between
scan poses. We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality by an
entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.Comment: Published in IEEE Robotics and Automation Letters, 201
Fast Monte-Carlo Localization on Aerial Vehicles using Approximate Continuous Belief Representations
Size, weight, and power constrained platforms impose constraints on
computational resources that introduce unique challenges in implementing
localization algorithms. We present a framework to perform fast localization on
such platforms enabled by the compressive capabilities of Gaussian Mixture
Model representations of point cloud data. Given raw structural data from a
depth sensor and pitch and roll estimates from an on-board attitude reference
system, a multi-hypothesis particle filter localizes the vehicle by exploiting
the likelihood of the data originating from the mixture model. We demonstrate
analysis of this likelihood in the vicinity of the ground truth pose and detail
its utilization in a particle filter-based vehicle localization strategy, and
later present results of real-time implementations on a desktop system and an
off-the-shelf embedded platform that outperform localization results from
running a state-of-the-art algorithm on the same environment
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Map-centric SLAM is emerging as an alternative of conventional graph-based
SLAM for its accuracy and efficiency in long-term mapping problems. However, in
map-centric SLAM, the process of loop closure differs from that of conventional
SLAM and the result of incorrect loop closure is more destructive and is not
reversible. In this paper, we present a tightly coupled photogeometric metric
localization for the loop closure problem in map-centric SLAM. In particular,
our method combines complementary constraints from LiDAR and camera sensors,
and validates loop closure candidates with sequential observations. The
proposed method provides a visual evidence-based outlier rejection where
failures caused by either place recognition or localization outliers can be
effectively removed. We demonstrate the proposed method is not only more
accurate than the conventional global ICP methods but is also robust to
incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY
201
OLT: A Toolkit for Object Labeling Applied to Robotic RGB-D Datasets
In this work we present the Object Labeling Toolkit
(OLT), a set of software components publicly available for
helping in the management and labeling of sequential RGB-D
observations collected by a mobile robot. Such a robot can be
equipped with an arbitrary number of RGB-D devices, possibly
integrating other sensors (e.g. odometry, 2D laser scanners,
etc.). OLT first merges the robot observations to generate a
3D reconstruction of the scene from which object segmentation
and labeling is conveniently accomplished. The annotated labels
are automatically propagated by the toolkit to each RGB-D
observation in the collected sequence, providing a dense labeling
of both intensity and depth images. The resulting objects’ labels
can be exploited for many robotic oriented applications, including
high-level decision making, semantic mapping, or contextual
object recognition. Software components within OLT are highly
customizable and expandable, facilitating the integration of
already-developed algorithms. To illustrate the toolkit suitability,
we describe its application to robotic RGB-D sequences taken in
a home environment.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech. Spanish grant pro-
gram FPU-MICINN 2010 and the Spanish projects TAROTH:
New developments toward a Robot at Home (DPI2011-25483)
and PROMOVE: Advances in mobile robotics for promoting
independent life of elders (DPI2014-55826-R
FLAT2D: Fast localization from approximate transformation into 2D
Many autonomous vehicles require precise localization into a prior map in order to support planning and to leverage semantic information within those maps (e.g. that the right lane is a turn-only lane.) A popular approach in automotive systems is to use infrared intensity maps of the ground surface to localize, making them susceptible to failures when the surface is obscured by snow or when the road is repainted. An emerging alternative is to localize based on the 3D structure around the vehicle; these methods are robust to these types of changes, but the maps are costly both in terms of storage and the computational cost of matching. In this paper, we propose a fast method for localizing based on 3D structure around the vehicle using a 2D representation. This representation retains many of the advantages of "full" matching in 3D, but comes with dramatically lower space and computational requirements. We also introduce a variation of Graph-SLAM tailored to support localization, allowing us to make use of graph-based error-recovery techniques in our localization estimate. Finally, we present real-world localization results for both an indoor mobile robotic platform and an autonomous golf cart, demonstrating that autonomous vehicles do not need full 3D matching to accurately localize in the environment
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