21,841 research outputs found
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
Accurate localization of other traffic participants is a vital task in
autonomous driving systems. State-of-the-art systems employ a combination of
sensing modalities such as RGB cameras and LiDARs for localizing traffic
participants, but most such demonstrations have been confined to plain roads.
We demonstrate, to the best of our knowledge, the first results for monocular
object localization and shape estimation on surfaces that do not share the same
plane with the moving monocular camera. We approximate road surfaces by local
planar patches and use semantic cues from vehicles in the scene to initialize a
local bundle-adjustment like procedure that simultaneously estimates the pose
and shape of the vehicles, and the orientation of the local ground plane on
which the vehicle stands as well. We evaluate the proposed approach on the
KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations.
The proposed approach significantly improves the state-of-the-art for monocular
object localization on arbitrarily-shaped roads.Comment: Submitted to IROS 201
Adaptive Lighting for Data-Driven Non-Line-of-Sight 3D Localization and Object Identification
Non-line-of-sight (NLOS) imaging of objects not visible to either the camera
or illumination source is a challenging task with vital applications including
surveillance and robotics. Recent NLOS reconstruction advances have been
achieved using time-resolved measurements which requires expensive and
specialized detectors and laser sources. In contrast, we propose a data-driven
approach for NLOS 3D localization and object identification requiring only a
conventional camera and projector. To generalize to complex line-of-sight (LOS)
scenes with non-planar surfaces and occlusions, we introduce an adaptive
lighting algorithm. This algorithm, based on radiosity, identifies and
illuminates scene patches in the LOS which most contribute to the NLOS light
paths, and can factor in system power constraints. We achieve an average
identification of 87.1% object identification for four classes of objects, and
average localization of the NLOS object's centroid with a mean-squared error
(MSE) of 1.97 cm in the occluded region for real data taken from a hardware
prototype. These results demonstrate the advantage of combining the physics of
light transport with active illumination for data-driven NLOS imaging
LMap: Shape-Preserving Local Mappings for Biomedical Visualization
Visualization of medical organs and biological structures is a challenging
task because of their complex geometry and the resultant occlusions. Global
spherical and planar mapping techniques simplify the complex geometry and
resolve the occlusions to aid in visualization. However, while resolving the
occlusions these techniques do not preserve the geometric context, making them
less suitable for mission-critical biomedical visualization tasks. In this
paper, we present a shape-preserving local mapping technique for resolving
occlusions locally while preserving the overall geometric context. More
specifically, we present a novel visualization algorithm, LMap, for conformally
parameterizing and deforming a selected local region-of-interest (ROI) on an
arbitrary surface. The resultant shape-preserving local mappings help to
visualize complex surfaces while preserving the overall geometric context. The
algorithm is based on the robust and efficient extrinsic Ricci flow technique,
and uses the dynamic Ricci flow algorithm to guarantee the existence of a local
map for a selected ROI on an arbitrary surface. We show the effectiveness and
efficacy of our method in three challenging use cases: (1) multimodal brain
visualization, (2) optimal coverage of virtual colonoscopy centerline
flythrough, and (3) molecular surface visualization.Comment: IEEE Transactions on Visualization and Computer Graphics, 24(12):
3111-3122, 2018 (12 pages, 11 figures
Cheap or Robust? The Practical Realization of Self-Driving Wheelchair Technology
To date, self-driving experimental wheelchair technologies have been either
inexpensive or robust, but not both. Yet, in order to achieve real-world
acceptance, both qualities are fundamentally essential. We present a unique
approach to achieve inexpensive and robust autonomous and semi-autonomous
assistive navigation for existing fielded wheelchairs, of which there are
approximately 5 million units in Canada and United States alone. Our prototype
wheelchair platform is capable of localization and mapping, as well as robust
obstacle avoidance, using only a commodity RGB-D sensor and wheel odometry. As
a specific example of the navigation capabilities, we focus on the single most
common navigation problem: the traversal of narrow doorways in arbitrary
environments. The software we have developed is generalizable to corridor
following, desk docking, and other navigation tasks that are either extremely
difficult or impossible for people with upper-body mobility impairments.Comment: In Proceedings of the IEEE International Conference on Rehabilitation
Robotics (ICORR'17), London, United Kingdom, Jul. 17-20, 201
Uncalibrated 3D Room Reconstruction from Sound
This paper presents a method to reconstruct the 3D structure of generic
convex rooms from sound signals. Differently from most of the previous
approaches, the method is fully uncalibrated in the sense that no knowledge
about the microphones and sources position is needed. Moreover, we demonstrate
that it is possible to bypass the well known echo labeling problem, allowing to
reconstruct the room shape in a reasonable computation time without the need of
additional hypotheses on the echoes order of arrival. Finally, the method is
intrinsically robust to outliers and missing data in the echoes detection,
allowing to work also in low SNR conditions. The proposed pipeline formalises
the problem in different steps such as time of arrival estimation, microphones
and sources localization and walls estimation. After providing a solution to
these different problems we present a global optimization approach that links
together all the problems in a single optimization function. The accuracy and
robustness of the method is assessed on a wide set of simulated setups and in a
challenging real scenario. Moreover we make freely available for a challenging
dataset for 3D room reconstruction with accurate ground truth in a real
scenario.Comment: The present work has been submitted to IEEE/ACM Transactions on Audio
Speech and Language Processin
A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces
Multiple lidars are prevalently used on mobile vehicles for rendering a broad
view to enhance the performance of localization and perception systems.
However, precise calibration of multiple lidars is challenging since the
feature correspondences in scan points cannot always provide enough
constraints. To address this problem, the existing methods require fixed
calibration targets in scenes or rely exclusively on additional sensors. In
this paper, we present a novel method that enables automatic lidar calibration
without these restrictions. Three linearly independent planar surfaces
appearing in surroundings is utilized to find correspondences. Two components
are developed to ensure the extrinsic parameters to be found: a closed-form
solver for initialization and an optimizer for refinement by minimizing a
nonlinear cost function. Simulation and experimental results demonstrate the
high accuracy of our calibration approach with the rotation and translation
errors smaller than 0.05rad and 0.1m respectively.Comment: 6 pages, 8 figures, accepted by 2019 IEEE Intelligent Vehicles
Symposium (IVS
Ground Edge based LIDAR Localization without a Reflectivity Calibration for Autonomous Driving
In this work we propose an alternative formulation to the problem of ground
reflectivity grid based localization involving laser scanned data from multiple
LIDARs mounted on autonomous vehicles. The driving idea of our localization
formulation is an alternative edge reflectivity grid representation which is
invariant to laser source, angle of incidence, range and robot surveying
motion. Such property eliminates the need of the post-factory reflectivity
calibration whose time requirements are infeasible in mass produced
robots/vehicles. Our experiments demonstrate that we can achieve better
performance than state of the art on ground reflectivity inference-map based
localization at no additional computational burden
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps
Lidar has become an essential sensor for autonomous driving as it provides
reliable depth estimation. Lidar is also the primary sensor used in building 3D
maps which can be used even in the case of low-cost systems which do not use
Lidar. Computation on Lidar point clouds is intensive as it requires processing
of millions of points per second. Additionally there are many subsequent tasks
such as clustering, detection, tracking and classification which makes
real-time execution challenging. In this paper, we discuss real-time dynamic
object detection algorithms which leverages previously mapped Lidar point
clouds to reduce processing. The prior 3D maps provide a static background
model and we formulate dynamic object detection as a background subtraction
problem. Computation and modeling challenges in the mapping and online
execution pipeline are described. We propose a rejection cascade architecture
to subtract road regions and other 3D regions separately. We implemented an
initial version of our proposed algorithm and evaluated the accuracy on CARLA
simulator.Comment: Preprint Submission to ECCVW AutoNUE 2018 - v2 author name accent
correctio
3D Scan Registration using Curvelet Features in Planetary Environments
Topographic mapping in planetary environments relies on accurate 3D scan
registration methods. However, most global registration algorithms relying on
features such as FPFH and Harris-3D show poor alignment accuracy in these
settings due to the poor structure of the Mars-like terrain and variable
resolution, occluded, sparse range data that is hard to register without some
a-priori knowledge of the environment. In this paper, we propose an alternative
approach to 3D scan registration using the curvelet transform that performs
multi-resolution geometric analysis to obtain a set of coefficients indexed by
scale (coarsest to finest), angle and spatial position. Features are detected
in the curvelet domain to take advantage of the directional selectivity of the
transform. A descriptor is computed for each feature by calculating the 3D
spatial histogram of the image gradients, and nearest neighbor based matching
is used to calculate the feature correspondences. Correspondence rejection
using Random Sample Consensus identifies inliers, and a locally optimal
Singular Value Decomposition-based estimation of the rigid-body transformation
aligns the laser scans given the re-projected correspondences in the metric
space. Experimental results on a publicly available data-set of planetary
analogue indoor facility, as well as simulated and real-world scans from Neptec
Design Group's IVIGMS 3D laser rangefinder at the outdoor CSA Mars yard
demonstrates improved performance over existing methods in the challenging
sparse Mars-like terrain.Comment: 27 pages in Journal of Field Robotics, 201
Indoor dense depth map at drone hovering
Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recent
years. Autonomous flight in indoor requires a dense depth map for navigable
space detection which is the fundamental component for autonomous navigation.
In this paper, we address the problem of reconstructing dense depth while a
drone is hovering (small camera motion) in indoor scenes using already
estimated cameras and sparse point cloud obtained from a vSLAM. We start by
segmenting the scene based on sudden depth variation using sparse 3D points and
introduce a patch-based local plane fitting via energy minimization which
combines photometric consistency and co-planarity with neighbouring patches.
The method also combines a plane sweep technique for image segments having
almost no sparse point for initialization. Experiments show, the proposed
method produces better depth for indoor in artificial lighting condition,
low-textured environment compared to earlier literature in small motion.Comment: Published on ICIP 201
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