55 research outputs found
The Importance of Coordinate Frames in Dynamic SLAM
Most Simultaneous localisation and mapping (SLAM) systems have traditionally
assumed a static world, which does not align with real-world scenarios. To
enable robots to safely navigate and plan in dynamic environments, it is
essential to employ representations capable of handling moving objects. Dynamic
SLAM is an emerging field in SLAM research as it improves the overall system
accuracy while providing additional estimation of object motions.
State-of-the-art literature informs two main formulations for Dynamic SLAM,
representing dynamic object points in either the world or object coordinate
frame. While expressing object points in a local reference frame may seem
intuitive, it may not necessarily lead to the most accurate and robust
solutions. This paper conducts and presents a thorough analysis of various
Dynamic SLAM formulations, identifying the best approach to address the
problem. To this end, we introduce a front-end agnostic framework using GTSAM
that can be used to evaluate various Dynamic SLAM formulations.Comment: 7 pages, 4 figures, submitted to ICRA 202
The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping
In this paper we present a novel data structure, the Bayes tree, which exploits the connections between graphical model inference and sparse linear algebra. The proposed data structure provides a new perspective on an entire class of simultaneous localization and mapping (SLAM) algorithms. Similar to a junction tree, a Bayes tree encodes a factored probability density, but unlike the junction tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. This makes it eminently suited to encode the sparse nature of the problem, especially in a smoothing and mapping (SAM) context. The inherent sparsity of SAM has already been exploited in the literature to produce efficient solutions in both batch and online mapping. The graphical model perspective allows us to develop a novel incremental algorithm that seamlessly incorporates reordering and relinearization. This obviates the need for expensive periodic batch operations from previous approaches, which negatively affect the performance and detract from the intended online nature of the algorithm. The new method is evaluated using simulated and real-world datasets in both landmark and pose SLAM settings
A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras
This paper proposes an observer for generating depth maps of a scene from a
sequence of measurements acquired by a two-plane light-field (plenoptic)
camera. The observer is based on a gradient-descent methodology. The use of
motion allows for estimation of depth maps where the scene contains
insufficient texture for static estimation methods to work. A rigourous
analysis of stability of the observer error is provided, and the observer is
tested in simulation, demonstrating convergence behaviour.Comment: Full version of paper submitted to CDC 2018. 11 pages. 12 figure
PEBO-SLAM: Observer design for visual inertial SLAM with convergence guarantees
This paper introduces a new linear parameterization to the problem of visual
inertial simultaneous localization and mapping (VI-SLAM) -- without any
approximation -- for the case only using information from a single monocular
camera and an inertial measurement unit. In this problem set, the system state
evolves on the nonlinear manifold , on which we
design dynamic extensions carefully to generate invariant foliations, such that
the problem can be reformulated into online \emph{constant parameter}
identification, then interestingly with linear regression models obtained. It
demonstrates that VI-SLAM can be translated into a linear least squares
problem, in the deterministic sense, \emph{globally} and \emph{exactly}. Based
on this observation, we propose a novel SLAM observer, following the recently
established parameter estimation-based observer (PEBO) methodology. A notable
merit is that the proposed observer enjoys almost global asymptotic stability,
requiring neither persistency of excitation nor uniform complete observability,
which, however, are widely adopted in most existing works with provable
stability but can hardly be assured in many practical scenarios
Calibrating Focused Light-Field Cameras Using Plenoptic Disc Features
This paper proposes a new method for estimating calibration
parameters of plenoptic cameras by minimizing the
nonlinear plenoptic reprojection error. Novel plenoptic feature
types are proposed as data for the calibration method.
These plenoptic disc features are in a natural one-to-one
correspondence with physical points in front of the camera.
We exploit the intrinsic geometry of plenoptic cameras
in a novel projection model that relates the plenoptic disc
features to physical points. The resulting calibration quality,
as quantified by mean reprojection error and 3D reconstruction
error, outperforms recently published results
iSAM2 : incremental smoothing and mapping using the Bayes tree
Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419.We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of
existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique
tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the
square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three
insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in
terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple
editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm
for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental
variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of
iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent
mapping algorithms in both quality and efficiency.M. Kaess, H. Johannsson and J. Leonard were partially supported
by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by
NSF, award number 0713162, “RI: Inference in Large-Scale
Graphical Models”. V. Ila has been partially supported by the
Spanish MICINN under the Programa Nacional de Movilidad
de Recursos Humanos de Investigación
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