33,999 research outputs found
Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
Modern 3D laser-range scanners have a high data rate, making online
simultaneous localization and mapping (SLAM) computationally challenging.
Recursive state estimation techniques are efficient but commit to a state
estimate immediately after a new scan is made, which may lead to misalignments
of measurements. We present a 3D SLAM approach that allows for refining
alignments during online mapping. Our method is based on efficient local
mapping and a hierarchical optimization back-end. Measurements of a 3D laser
scanner are aggregated in local multiresolution maps by means of surfel-based
registration. The local maps are used in a multi-level graph for allocentric
mapping and localization. In order to incorporate corrections when refining the
alignment, the individual 3D scans in the local map are modeled as a sub-graph
and graph optimization is performed to account for drift and misalignments in
the local maps. Furthermore, in each sub-graph, a continuous-time
representation of the sensor trajectory allows to correct measurements between
scan poses. We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality by an
entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
Incremental Sparse GP Regression for Continuous-time Trajectory Estimation & Mapping
Recent work on simultaneous trajectory estimation and mapping (STEAM) for
mobile robots has found success by representing the trajectory as a Gaussian
process. Gaussian processes can represent a continuous-time trajectory,
elegantly handle asynchronous and sparse measurements, and allow the robot to
query the trajectory to recover its estimated position at any time of interest.
A major drawback of this approach is that STEAM is formulated as a batch
estimation problem. In this paper we provide the critical extensions necessary
to transform the existing batch algorithm into an extremely efficient
incremental algorithm. In particular, we are able to vastly speed up the
solution time through efficient variable reordering and incremental sparse
updates, which we believe will greatly increase the practicality of Gaussian
process methods for robot mapping and localization. Finally, we demonstrate the
approach and its advantages on both synthetic and real datasets.Comment: 10 pages, 10 figure
Pseudospectral Model Predictive Control under Partially Learned Dynamics
Trajectory optimization of a controlled dynamical system is an essential part
of autonomy, however many trajectory optimization techniques are limited by the
fidelity of the underlying parametric model. In the field of robotics, a lack
of model knowledge can be overcome with machine learning techniques, utilizing
measurements to build a dynamical model from the data. This paper aims to take
the middle ground between these two approaches by introducing a semi-parametric
representation of the underlying system dynamics. Our goal is to leverage the
considerable information contained in a traditional physics based model and
combine it with a data-driven, non-parametric regression technique known as a
Gaussian Process. Integrating this semi-parametric model with model predictive
pseudospectral control, we demonstrate this technique on both a cart pole and
quadrotor simulation with unmodeled damping and parametric error. In order to
manage parametric uncertainty, we introduce an algorithm that utilizes Sparse
Spectrum Gaussian Processes (SSGP) for online learning after each rollout. We
implement this online learning technique on a cart pole and quadrator, then
demonstrate the use of online learning and obstacle avoidance for the dubin
vehicle dynamics.Comment: Accepted but withdrawn from AIAA Scitech 201
Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression
In this paper, we revisit batch state estimation through the lens of Gaussian
process (GP) regression. We consider continuous-discrete estimation problems
wherein a trajectory is viewed as a one-dimensional GP, with time as the
independent variable. Our continuous-time prior can be defined by any
nonlinear, time-varying stochastic differential equation driven by white noise;
this allows the possibility of smoothing our trajectory estimates using a
variety of vehicle dynamics models (e.g., `constant-velocity'). We show that
this class of prior results in an inverse kernel matrix (i.e., covariance
matrix between all pairs of measurement times) that is exactly sparse
(block-tridiagonal) and that this can be exploited to carry out GP regression
(and interpolation) very efficiently. When the prior is based on a linear,
time-varying stochastic differential equation and the measurement model is also
linear, this GP approach is equivalent to classical, discrete-time smoothing
(at the measurement times); when a nonlinearity is present, we iterate over the
whole trajectory to maximize accuracy. We test the approach experimentally on a
simultaneous trajectory estimation and mapping problem using a mobile robot
dataset.Comment: Submitted to Autonomous Robots on 20 November 2014, manuscript #
AURO-D-14-00185, 16 pages, 7 figure
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