3 research outputs found
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
In this work we present a novel framework that uses deep learning to predict
object feature points that are out-of-view in the input image. This system was
developed with the application of model-based tracking in mind, particularly in
the case of autonomous inspection robots, where only partial views of the
object are available. Out-of-view prediction is enabled by applying scaling to
the feature point labels during network training. This is combined with a
recurrent neural network architecture designed to provide the final prediction
layers with rich feature information from across the spatial extent of the
input image. To show the versatility of these out-of-view predictions, we
describe how to integrate them in both a particle filter tracker and an
optimisation based tracker. To evaluate our work we compared our framework with
one that predicts only points inside the image. We show that as the amount of
the object in view decreases, being able to predict outside the image bounds
adds robustness to the final pose estimation.Comment: Submitted to IROS 201
Improving drone localisation around wind turbines using monocular model-based tracking
We present a novel method of integrating image-based measurements into a
drone navigation system for the automated inspection of wind turbines. We take
a model-based tracking approach, where a 3D skeleton representation of the
turbine is matched to the image data. Matching is based on comparing the
projection of the representation to that inferred from images using a
convolutional neural network. This enables us to find image correspondences
using a generic turbine model that can be applied to a wide range of turbine
shapes and sizes. To estimate 3D pose of the drone, we fuse the network output
with GPS and IMU measurements using a pose graph optimiser. Results illustrate
that the use of the image measurements significantly improves the accuracy of
the localisation over that obtained using GPS and IMU alone.Comment: Accepted at for the International Conference on Robotics and
Automatio