3,804 research outputs found
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
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
Robust and Fast 3D Scan Alignment using Mutual Information
This paper presents a mutual information (MI) based algorithm for the
estimation of full 6-degree-of-freedom (DOF) rigid body transformation between
two overlapping point clouds. We first divide the scene into a 3D voxel grid
and define simple to compute features for each voxel in the scan. The two scans
that need to be aligned are considered as a collection of these features and
the MI between these voxelized features is maximized to obtain the correct
alignment of scans. We have implemented our method with various simple point
cloud features (such as number of points in voxel, variance of z-height in
voxel) and compared the performance of the proposed method with existing
point-to-point and point-to- distribution registration methods. We show that
our approach has an efficient and fast parallel implementation on GPU, and
evaluate the robustness and speed of the proposed algorithm on two real-world
datasets which have variety of dynamic scenes from different environments
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
The ability to build maps is a key functionality for the majority of mobile
robots. A central ingredient to most mapping systems is the registration or
alignment of the recorded sensor data. In this paper, we present a general
methodology for photometric registration that can deal with multiple different
cues. We provide examples for registering RGBD as well as 3D LIDAR data. In
contrast to popular point cloud registration approaches such as ICP our method
does not rely on explicit data association and exploits multiple modalities
such as raw range and image data streams. Color, depth, and normal information
are handled in an uniform manner and the registration is obtained by minimizing
the pixel-wise difference between two multi-channel images. We developed a
flexible and general framework and implemented our approach inside that
framework. We also released our implementation as open source C++ code. The
experiments show that our approach allows for an accurate registration of the
sensor data without requiring an explicit data association or model-specific
adaptations to datasets or sensors. Our approach exploits the different cues in
a natural and consistent way and the registration can be done at framerate for
a typical range or imaging sensor.Comment: 8 page
An ICP variant using a point-to-line metric
This paper describes PLICP, an ICP (iterative closest/corresponding point) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric. The resulting algorithm has some interesting properties: it converges quadratically, and in a finite number of steps. The method is validated against vanilla ICP, IDC (iterative dual correspondences), and MBICP (Metric-Based ICP) by reproducing the experiments performed in Minguez et al. (2006). The experiments suggest that PLICP is more precise, and requires less iterations. However, it is less robust to very large initial displacement errors. The last part of the paper is devoted to purely algorithmic optimization of the correspondence search; this allows for a significant speed-up of the computation. The source code is available for download
NICP: Dense normal based point cloud registration
In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches
A new method for aspherical surface fitting with large-volume datasets
In the framework of form characterization of aspherical surfaces, European National Metrology Institutes (NMIs) have been developing ultra-high precision machines having the ability to measure aspherical lenses with an uncertainty of few tens of nanometers. The fitting of the acquired aspherical datasets onto their corresponding theoretical model should be achieved at the same level of precision. In this article, three fitting algorithms are investigated: the Limited memory-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), the Levenberg–Marquardt (LM) and one variant of the Iterative Closest Point (ICP). They are assessed based on their capacities to converge relatively fast to achieve a nanometric level of accuracy, to manage a large volume of data and to be robust to the position of the data with respect to the model. Nev-ertheless, the algorithms are first evaluated on simulated datasets and their performances are studied. The comparison of these algorithms is extended on measured datasets of an aspherical lens. The results validate the newly used method for the fitting of aspherical surfaces and reveal that it is well adapted, faster and less complex than the LM or ICP methods.EMR
3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation
Global registration of heterogeneous ground and aerial mapping data is a
challenging task. This is especially difficult in disaster response scenarios
when we have no prior information on the environment and cannot assume the
regular order of man-made environments or meaningful semantic cues. In this
work we extensively evaluate different approaches to globally register UGV
generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud
maps from vision sensors. The approaches are realizations of different
selections for: a) local features: key-points or segments; b) descriptors:
FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR.
Additionally, we compare the results against standard approaches like applying
ICP after a good prior transformation has been given. The evaluation criteria
include the distance which a UGV needs to travel to successfully localize, the
registration error, and the computational cost. In this context, we report our
findings on effectively performing the task on two new Search and Rescue
datasets. Our results have the potential to help the community take informed
decisions when registering point-cloud maps from ground robots to those from
aerial robots.Comment: Awarded Best Paper at the 15th IEEE International Symposium on
Safety, Security, and Rescue Robotics 2017 (SSRR 2017
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