13 research outputs found
Convex Relaxations for Pose Graph Optimization with Outliers
Pose Graph Optimization involves the estimation of a set of poses from
pairwise measurements and provides a formalization for many problems arising in
mobile robotics and geometric computer vision. In this paper, we consider the
case in which a subset of the measurements fed to pose graph optimization is
spurious. Our first contribution is to develop robust estimators that can cope
with heavy-tailed measurement noise, hence increasing robustness to the
presence of outliers. Since the resulting estimators require solving nonconvex
optimization problems, we further develop convex relaxations that approximately
solve those problems via semidefinite programming. We then provide conditions
under which the proposed relaxations are exact. Contrarily to existing
approaches, our convex relaxations do not rely on the availability of an
initial guess for the unknown poses, hence they are more suitable for setups in
which such guess is not available (e.g., multi-robot localization, recovery
after localization failure). We tested the proposed techniques in extensive
simulations, and we show that some of the proposed relaxations are indeed tight
(i.e., they solve the original nonconvex problem 10 exactly) and ensure
accurate estimation in the face of a large number of outliers.Comment: 10 pages, 5 figures, accepted for publication in the IEEE Robotics
and Automation Letters, 201
Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models
Perceptual aliasing is one of the main causes of failure for Simultaneous
Localization and Mapping (SLAM) systems operating in the wild. Perceptual
aliasing is the phenomenon where different places generate a similar visual
(or, in general, perceptual) footprint. This causes spurious measurements to be
fed to the SLAM estimator, which typically results in incorrect localization
and mapping results. The problem is exacerbated by the fact that those outliers
are highly correlated, in the sense that perceptual aliasing creates a large
number of mutually-consistent outliers. Another issue stems from the fact that
most state-of-the-art techniques rely on a given trajectory guess (e.g., from
odometry) to discern between inliers and outliers and this makes the resulting
pipeline brittle, since the accumulation of error may result in incorrect
choices and recovery from failures is far from trivial. This work provides a
unified framework to model perceptual aliasing in SLAM and provides practical
algorithms that can cope with outliers without relying on any initial guess. We
present two main contributions. The first is a Discrete-Continuous Graphical
Model (DC-GM) for SLAM: the continuous portion of the DC-GM captures the
standard SLAM problem, while the discrete portion describes the selection of
the outliers and models their correlation. The second contribution is a
semidefinite relaxation to perform inference in the DC-GM that returns
estimates with provable sub-optimality guarantees. Experimental results on
standard benchmarking datasets show that the proposed technique compares
favorably with state-of-the-art methods while not relying on an initial guess
for optimization.Comment: 13 pages, 14 figures, 1 tabl
DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
To achieve collaborative tasks, robots in a team need to have a shared
understanding of the environment and their location within it. Distributed
Simultaneous Localization and Mapping (SLAM) offers a practical solution to
localize the robots without relying on an external positioning system (e.g.
GPS) and with minimal information exchange. Unfortunately, current distributed
SLAM systems are vulnerable to perception outliers and therefore tend to use
very conservative parameters for inter-robot place recognition. However, being
too conservative comes at the cost of rejecting many valid loop closure
candidates, which results in less accurate trajectory estimates. This paper
introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection
mechanism that can work with less conservative parameters. DOOR-SLAM is based
on peer-to-peer communication and does not require full connectivity among the
robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined
with a distributed pairwise consistent measurement set maximization algorithm
to reject spurious inter-robot loop closures; and a distributed SLAM front-end
that detects inter-robot loop closures without exchanging raw sensor data. The
system has been evaluated in simulations, benchmarking datasets, and field
experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM
produces more inter-robot loop closures, successfully rejects outliers, and
results in accurate trajectory estimates, while requiring low communication
bandwidth. Full source code is available at
https://github.com/MISTLab/DOOR-SLAM.git.Comment: 8 pages, 11 figures, 2 table
Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
We introduce Tempered Geodesic Markov Chain Monte Carlo (TG-MCMC) algorithm
for initializing pose graph optimization problems, arising in various scenarios
such as SFM (structure from motion) or SLAM (simultaneous localization and
mapping). TG-MCMC is first of its kind as it unites asymptotically global
non-convex optimization on the spherical manifold of quaternions with posterior
sampling, in order to provide both reliable initial poses and uncertainty
estimates that are informative about the quality of individual solutions. We
devise rigorous theoretical convergence guarantees for our method and
extensively evaluate it on synthetic and real benchmark datasets. Besides its
elegance in formulation and theory, we show that our method is robust to
missing data, noise and the estimated uncertainties capture intuitive
properties of the data.Comment: Published at NeurIPS 2018, 25 pages with supplement
Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection
Semidefinite Programming (SDP) and Sums-of-Squares (SOS) relaxations have led
to certifiably optimal non-minimal solvers for several robotics and computer
vision problems. However, most non-minimal solvers rely on least-squares
formulations, and, as a result, are brittle against outliers. While a standard
approach to regain robustness against outliers is to use robust cost functions,
the latter typically introduce other non-convexities, preventing the use of
existing non-minimal solvers. In this paper, we enable the simultaneous use of
non-minimal solvers and robust estimation by providing a general-purpose
approach for robust global estimation, which can be applied to any problem
where a non-minimal solver is available for the outlier-free case. To this end,
we leverage the Black-Rangarajan duality between robust estimation and outlier
processes (which has been traditionally applied to early vision problems), and
show that graduated non-convexity (GNC) can be used in conjunction with
non-minimal solvers to compute robust solutions, without requiring an initial
guess. Although GNC's global optimality cannot be guaranteed, we demonstrate
the empirical robustness of the resulting robust non-minimal solvers in
applications, including point cloud and mesh registration, pose graph
optimization, and image-based object pose estimation (also called shape
alignment). Our solvers are robust to 70-80% of outliers, outperform RANSAC,
are more accurate than specialized local solvers, and faster than specialized
global solvers. We also propose the first certifiably optimal non-minimal
solver for shape alignment using SOS relaxation.Comment: 10 pages, 5 figures, published at IEEE Robotics and Automation
Letters (RA-L), 2020, Best Paper Award in Robot Vision at ICRA 202
Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms
Recent advances in computer vision and deep learning have shown promising
performance in estimating rigid/similarity transformation between unregistered
point clouds of complex objects and scenes. However, their performances are
mostly evaluated using a limited number of datasets from a single sensor (e.g.
Kinect or RealSense cameras), lacking a comprehensive overview of their
applicability in photogrammetric 3D mapping scenarios. In this work, we provide
a comprehensive review of the state-of-the-art (SOTA) point cloud registration
methods, where we analyze and evaluate these methods using a diverse set of
point cloud data from indoor to satellite sources. The quantitative analysis
allows for exploring the strengths, applicability, challenges, and future
trends of these methods. In contrast to existing analysis works that introduce
point cloud registration as a holistic process, our experimental analysis is
based on its inherent two-step process to better comprehend these approaches
including feature/keypoint-based initial coarse registration and dense fine
registration through cloud-to-cloud (C2C) optimization. More than ten methods,
including classic hand-crafted, deep-learning-based feature correspondence, and
robust C2C methods were tested. We observed that the success rate of most of
the algorithms are fewer than 40% over the datasets we tested and there are
still are large margin of improvement upon existing algorithms concerning 3D
sparse corresopondence search, and the ability to register point clouds with
complex geometry and occlusions. With the evaluated statistics on three
datasets, we conclude the best-performing methods for each step and provide our
recommendations, and outlook future efforts.Comment: 7 figure
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
This paper presents Kimera-Multi, the first multi-robot system that (i) is
robust and capable of identifying and rejecting incorrect inter and intra-robot
loop closures resulting from perceptual aliasing, (ii) is fully distributed and
only relies on local (peer-to-peer) communication to achieve distributed
localization and mapping, and (iii) builds a globally consistent
metric-semantic 3D mesh model of the environment in real-time, where faces of
the mesh are annotated with semantic labels. Kimera-Multi is implemented by a
team of robots equipped with visual-inertial sensors. Each robot builds a local
trajectory estimate and a local mesh using Kimera. When communication is
available, robots initiate a distributed place recognition and robust pose
graph optimization protocol based on a novel distributed graduated
non-convexity algorithm. The proposed protocol allows the robots to improve
their local trajectory estimates by leveraging inter-robot loop closures while
being robust to outliers. Finally, each robot uses its improved trajectory
estimate to correct the local mesh using mesh deformation techniques.
We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking
datasets, and challenging outdoor datasets collected using ground robots. Both
real and simulated experiments involve long trajectories (e.g., up to 800
meters per robot). The experiments show that Kimera-Multi (i) outperforms the
state of the art in terms of robustness and accuracy, (ii) achieves estimation
errors comparable to a centralized SLAM system while being fully distributed,
(iii) is parsimonious in terms of communication bandwidth, (iv) produces
accurate metric-semantic 3D meshes, and (v) is modular and can be also used for
standard 3D reconstruction (i.e., without semantic labels) or for trajectory
estimation (i.e., without reconstructing a 3D mesh).Comment: Accepted by IEEE Transactions on Robotics (18 pages, 15 figures