18 research outputs found
Occlusion-Robust MVO: Multimotion Estimation Through Occlusion Via Motion Closure
Visual motion estimation is an integral and well-studied challenge in
autonomous navigation. Recent work has focused on addressing multimotion
estimation, which is especially challenging in highly dynamic environments.
Such environments not only comprise multiple, complex motions but also tend to
exhibit significant occlusion.
Previous work in object tracking focuses on maintaining the integrity of
object tracks but usually relies on specific appearance-based descriptors or
constrained motion models. These approaches are very effective in specific
applications but do not generalize to the full multimotion estimation problem.
This paper presents a pipeline for estimating multiple motions, including the
camera egomotion, in the presence of occlusions. This approach uses an
expressive motion prior to estimate the SE (3) trajectory of every motion in
the scene, even during temporary occlusions, and identify the reappearance of
motions through motion closure. The performance of this occlusion-robust
multimotion visual odometry (MVO) pipeline is evaluated on real-world data and
the Oxford Multimotion Dataset.Comment: To appear at the 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS). An earlier version of this work first
appeared at the Long-term Human Motion Planning Workshop (ICRA 2019). 8
pages, 5 figures. Video available at
https://www.youtube.com/watch?v=o_N71AA6FR
Batch Informed Trees (BIT*): Informed Asymptotically Optimal Anytime Search
Path planning in robotics often requires finding high-quality solutions to
continuously valued and/or high-dimensional problems. These problems are
challenging and most planning algorithms instead solve simplified
approximations. Popular approximations include graphs and random samples, as
respectively used by informed graph-based searches and anytime sampling-based
planners. Informed graph-based searches, such as A*, traditionally use
heuristics to search a priori graphs in order of potential solution quality.
This makes their search efficient but leaves their performance dependent on the
chosen approximation. If its resolution is too low then they may not find a
(suitable) solution but if it is too high then they may take a prohibitively
long time to do so. Anytime sampling-based planners, such as RRT*,
traditionally use random sampling to approximate the problem domain
incrementally. This allows them to increase resolution until a suitable
solution is found but makes their search dependent on the order of
approximation. Arbitrary sequences of random samples approximate the problem
domain in every direction simultaneously and but may be prohibitively
inefficient at containing a solution. This paper unifies and extends these two
approaches to develop Batch Informed Trees (BIT*), an informed, anytime
sampling-based planner. BIT* solves continuous path planning problems
efficiently by using sampling and heuristics to alternately approximate and
search the problem domain. Its search is ordered by potential solution quality,
as in A*, and its approximation improves indefinitely with additional
computational time, as in RRT*. It is shown analytically to be almost-surely
asymptotically optimal and experimentally to outperform existing sampling-based
planners, especially on high-dimensional planning problems.Comment: International Journal of Robotics Research (IJRR). 32 Pages. 16
Figure
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
Rapidly-exploring random trees (RRTs) are popular in motion planning because
they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s)
extend RRTs to the problem of finding the optimal solution, but in doing so
asymptotically find the optimal path from the initial state to every state in
the planning domain. This behaviour is not only inefficient but also
inconsistent with their single-query nature.
For problems seeking to minimize path length, the subset of states that can
improve a solution can be described by a prolate hyperspheroid. We show that
unless this subset is sampled directly, the probability of improving a solution
becomes arbitrarily small in large worlds or high state dimensions. In this
paper, we present an exact method to focus the search by directly sampling this
subset.
The advantages of the presented sampling technique are demonstrated with a
new algorithm, Informed RRT*. This method retains the same probabilistic
guarantees on completeness and optimality as RRT* while improving the
convergence rate and final solution quality. We present the algorithm as a
simple modification to RRT* that could be further extended by more advanced
path-planning algorithms. We show experimentally that it outperforms RRT* in
rate of convergence, final solution cost, and ability to find difficult
passages while demonstrating less dependence on the state dimension and range
of the planning problem.Comment: 8 pages, 11 figures. Videos available at
https://www.youtube.com/watch?v=d7dX5MvDYTc and
https://www.youtube.com/watch?v=nsl-5MZfwu
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm
based on unifying graph- and sampling-based planning techniques. By recognizing
that a set of samples describes an implicit random geometric graph (RGG), we
are able to combine the efficient ordered nature of graph-based techniques,
such as A*, with the anytime scalability of sampling-based algorithms, such as
Rapidly-exploring Random Trees (RRT).
BIT* uses a heuristic to efficiently search a series of increasingly dense
implicit RGGs while reusing previous information. It can be viewed as an
extension of incremental graph-search techniques, such as Lifelong Planning A*
(LPA*), to continuous problem domains as well as a generalization of existing
sampling-based optimal planners. It is shown that it is probabilistically
complete and asymptotically optimal.
We demonstrate the utility of BIT* on simulated random worlds in
and and manipulation problems on CMU's HERB, a
14-DOF two-armed robot. On these problems, BIT* finds better solutions faster
than RRT, RRT*, Informed RRT*, and Fast Marching Trees (FMT*) with faster
anytime convergence towards the optimum, especially in high dimensions.Comment: 8 Pages. 6 Figures. Video available at
http://www.youtube.com/watch?v=TQIoCC48gp
On Recursive Random Prolate Hyperspheroids
This technical note analyzes the properties of a random sequence of prolate
hyperspheroids with common foci. Each prolate hyperspheroid in the sequence is
defined by a sample drawn randomly from the previous volume such that the
sample lies on the new surface (Fig. 1). Section 1 defines the prolate
hyperspheroid coordinate system and the resulting differential volume, Section
2 calculates the expected value of the new transverse diameter given a uniform
distribution over the existing prolate hyperspheroid, and Section 3 calculates
the convergence rate of this sequence. For clarity, the differential volume and
some of the identities used in the integration are verified in Appendix A
through a calculation of the volume of a general prolate hyperspheroid.Comment: 11 pages, 2 figure
The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning
High-quality observations of the real world are crucial for a variety of
applications, including producing 3D printed replicas of small-scale scenes and
conducting inspections of large-scale infrastructure. These 3D observations are
commonly obtained by combining multiple sensor measurements from different
views. Guiding the selection of suitable views is known as the NBV planning
problem.
Most NBV approaches reason about measurements using rigid data structures
(e.g., surface meshes or voxel grids). This simplifies next best view selection
but can be computationally expensive, reduces real-world fidelity, and couples
the selection of a next best view with the final data processing.
This paper presents the Surface Edge Explorer, a NBV approach that selects
new observations directly from previous sensor measurements without requiring
rigid data structures. SEE uses measurement density to propose next best views
that increase coverage of insufficiently observed surfaces while avoiding
potential occlusions. Statistical results from simulated experiments show that
SEE can attain similar or better surface coverage with less observation time
and travel distance than evaluated volumetric approaches on both small- and
large-scale scenes. Real-world experiments demonstrate SEE autonomously
observing a deer statue using a 3D sensor affixed to a robotic arm.Comment: Under review for the International Journal of Robotics Research
(IJRR), Manuscript #IJR-22-4541. 25 pages, 17 figures, 6 tables. Videos
available at https://www.youtube.com/watch?v=dqppqRlaGEA and
https://www.youtube.com/playlist?list=PLbaQBz4TuPcyNh4COoaCtC1ZGhpbEkFE
Proactive Estimation of Occlusions and Scene Coverage for Planning Next Best Views in an Unstructured Representation
The process of planning views to observe a scene is known as the Next Best
View (NBV) problem. Approaches often aim to obtain high-quality scene
observations while reducing the number of views, travel distance and
computational cost.
Considering occlusions and scene coverage can significantly reduce the number
of views and travel distance required to obtain an observation. Structured
representations (e.g., a voxel grid or surface mesh) typically use raycasting
to evaluate the visibility of represented structures but this is often
computationally expensive. Unstructured representations (e.g., point density)
avoid the computational overhead of maintaining and raycasting a structure
imposed on the scene but as a result do not proactively predict the success of
future measurements.
This paper presents proactive solutions for handling occlusions and
considering scene coverage with an unstructured representation. Their
performance is evaluated by extending the density-based Surface Edge Explorer
(SEE). Experiments show that these techniques allow an unstructured
representation to observe scenes with fewer views and shorter distances while
retaining high observation quality and low computational cost.Comment: For a video of SEE++ go to
https://www.youtube.com/watch?v=4r2Z85zccms . For an open-source version of
SEE++ go to https://github.com/robotic-esp/see-public . Documentation can be
found at https://robotic-esp.github.io/see-publi
Event-based Visual Odometry with Full Temporal Resolution via Continuous-time Gaussian Process Regression
Event-based cameras asynchronously capture individual visual changes in a
scene. This makes them more robust than traditional frame-based cameras to
highly dynamic motions and poor illumination. It also means that every
measurement in a scene can occur at a unique time.
Handling these different measurement times is a major challenge of using
event-based cameras. It is often addressed in visual odometry (VO) pipelines by
approximating temporally close measurements as occurring at one common time.
This grouping simplifies the estimation problem but sacrifices the inherent
temporal resolution of event-based cameras.
This paper instead presents a complete stereo VO pipeline that estimates
directly with individual event-measurement times without requiring any grouping
or approximation. It uses continuous-time trajectory estimation to maintain the
temporal fidelity and asynchronous nature of event-based cameras through
Gaussian process regression with a physically motivated prior. Its performance
is evaluated on the MVSEC dataset, where it achieves 7.9e-3 and 5.9e-3 RMS
relative error on two independent sequences, outperforming the existing
publicly available event-based stereo VO pipeline by two and four times,
respectively.Comment: Submitted to IEEE Robotics and Automation Letters (RA-L). Manuscript
#23-1314. 8 pages, 4 figure