200 research outputs found
Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return.
We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians. We present a new algorithm for path matching, and test it in simulated assisted return tasks with data from WeAllWalk, the only existing data set with inertial data recorded from blind walkers. We consider two odometry systems, one based on deep learning (RoNIN), and the second based on robust turn detection and step counting. Our results show that the best path matching results are obtained using the turns/steps odometry system
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Inertial sensors play a pivotal role in indoor localization, which in turn
lays the foundation for pervasive personal applications. However, low-cost
inertial sensors, as commonly found in smartphones, are plagued by bias and
noise, which leads to unbounded growth in error when accelerations are double
integrated to obtain displacement. Small errors in state estimation propagate
to make odometry virtually unusable in a matter of seconds. We propose to break
the cycle of continuous integration, and instead segment inertial data into
independent windows. The challenge becomes estimating the latent states of each
window, such as velocity and orientation, as these are not directly observable
from sensor data. We demonstrate how to formulate this as an optimization
problem, and show how deep recurrent neural networks can yield highly accurate
trajectories, outperforming state-of-the-art shallow techniques, on a wide
range of tests and attachments. In particular, we demonstrate that IONet can
generalize to estimate odometry for non-periodic motion, such as a shopping
trolley or baby-stroller, an extremely challenging task for existing
techniques.Comment: To appear in AAAI18 (Oral
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression
Visual-inertial localization is a key problem in computer vision and robotics
applications such as virtual reality, self-driving cars, and aerial vehicles.
The goal is to estimate an accurate pose of an object when either the
environment or the dynamics are known. Recent methods directly regress the pose
using convolutional and spatio-temporal networks. Absolute pose regression
(APR) techniques predict the absolute camera pose from an image input in a
known scene. Odometry methods perform relative pose regression (RPR) that
predicts the relative pose from a known object dynamic (visual or inertial
inputs). The localization task can be improved by retrieving information of
both data sources for a cross-modal setup, which is a challenging problem due
to contradictory tasks. In this work, we conduct a benchmark to evaluate deep
multimodal fusion based on PGO and attention networks. Auxiliary and Bayesian
learning are integrated for the APR task. We show accuracy improvements for the
RPR-aided APR task and for the RPR-RPR task for aerial vehicles and hand-held
devices. We conduct experiments on the EuRoC MAV and PennCOSYVIO datasets, and
record a novel industry dataset.Comment: Under revie
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