11 research outputs found
Robust Inference for Visual-Inertial Sensor Fusion
Inference of three-dimensional motion from the fusion of inertial and visual
sensory data has to contend with the preponderance of outliers in the latter.
Robust filtering deals with the joint inference and classification task of
selecting which data fits the model, and estimating its state. We derive the
optimal discriminant and propose several approximations, some used in the
literature, others new. We compare them analytically, by pointing to the
assumptions underlying their approximations, and empirically. We show that the
best performing method improves the performance of state-of-the-art
visual-inertial sensor fusion systems, while retaining the same computational
complexity.Comment: Submitted to ICRA 2015, Manuscript #2912. Video results available at:
http://youtu.be/5JSF0-DbIR
PROBE-GK: Predictive Robust Estimation using Generalized Kernels
Many algorithms in computer vision and robotics make strong assumptions about
uncertainty, and rely on the validity of these assumptions to produce accurate
and consistent state estimates. In practice, dynamic environments may degrade
sensor performance in predictable ways that cannot be captured with static
uncertainty parameters. In this paper, we employ fast nonparametric Bayesian
inference techniques to more accurately model sensor uncertainty. By setting a
prior on observation uncertainty, we derive a predictive robust estimator, and
show how our model can be learned from sample images, both with and without
knowledge of the motion used to generate the data. We validate our approach
through Monte Carlo simulations, and report significant improvements in
localization accuracy relative to a fixed noise model in several settings,
including on synthetic data, the KITTI dataset, and our own experimental
platform.Comment: In Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA'16), Stockholm, Sweden, May 16-21, 201
On-Manifold Preintegration for Real-Time Visual-Inertial Odometry
Current approaches for visual-inertial odometry (VIO) are able to attain
highly accurate state estimation via nonlinear optimization. However, real-time
optimization quickly becomes infeasible as the trajectory grows over time, this
problem is further emphasized by the fact that inertial measurements come at
high rate, hence leading to fast growth of the number of variables in the
optimization. In this paper, we address this issue by preintegrating inertial
measurements between selected keyframes into single relative motion
constraints. Our first contribution is a \emph{preintegration theory} that
properly addresses the manifold structure of the rotation group. We formally
discuss the generative measurement model as well as the nature of the rotation
noise and derive the expression for the \emph{maximum a posteriori} state
estimator. Our theoretical development enables the computation of all necessary
Jacobians for the optimization and a-posteriori bias correction in analytic
form. The second contribution is to show that the preintegrated IMU model can
be seamlessly integrated into a visual-inertial pipeline under the unifying
framework of factor graphs. This enables the application of
incremental-smoothing algorithms and the use of a \emph{structureless} model
for visual measurements, which avoids optimizing over the 3D points, further
accelerating the computation. We perform an extensive evaluation of our
monocular \VIO pipeline on real and simulated datasets. The results confirm
that our modelling effort leads to accurate state estimation in real-time,
outperforming state-of-the-art approaches.Comment: 20 pages, 24 figures, accepted for publication in IEEE Transactions
on Robotics (TRO) 201
Autonomous Robots in Dynamic Indoor Environments: Localization and Person-Following
Autonomous social robots have many tasks that they need to address such as localization, mapping, navigation, person following, place recognition, etc. In this thesis we focus on two key components required for the navigation of autonomous robots namely, person following behaviour and localization in dynamic human environments. We propose three novel approaches to address these components; two approaches for person following and one for indoor localization. A convolutional neural networks based approach and an Ada-boost based approach are developed for person following. We demonstrate the results by showing the tracking accuracy over time for this behaviour. For the localization task, we propose a novel approach which can act as a wrapper for traditional visual odometry based approaches to improve the localization accuracy in dynamic human environments. We evaluate this approach by showing how the performance varies with increasing number of dynamic agents present in the scene. This thesis provides qualitative and quantitative evaluations for each of the approaches proposed and show that we perform better than the current approaches