6 research outputs found
A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups
Hand-eye calibration is the problem of estimating the spatial transformation
between a reference frame, usually the base of a robot arm or its gripper, and
the reference frame of one or multiple cameras. Generally, this calibration is
solved as a non-linear optimization problem, what instead is rarely done is to
exploit the underlying graph structure of the problem itself. Actually, the
problem of hand-eye calibration can be seen as an instance of the Simultaneous
Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we
present a pose-graph approach to the hand-eye calibration problem that extends
a recent state-of-the-art solution in two different ways: i) by formulating the
solution to eye-on-base setups with one camera; ii) by covering multi-camera
robotic setups. The proposed approach has been validated in simulation against
standard hand-eye calibration methods. Moreover, a real application is shown.
In both scenarios, the proposed approach overcomes all alternative methods. We
release with this paper an open-source implementation of our graph-based
optimization framework for multi-camera setups.Comment: This paper has been accepted for publication at the 2023 IEEE
International Conference on Robotics and Automation (ICRA
Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition
Autonomous navigation in underwater environments presents challenges due to
factors such as light absorption and water turbidity, limiting the
effectiveness of optical sensors. Sonar systems are commonly used for
perception in underwater operations as they are unaffected by these
limitations. Traditional computer vision algorithms are less effective when
applied to sonar-generated acoustic images, while convolutional neural networks
(CNNs) typically require large amounts of labeled training data that are often
unavailable or difficult to acquire. To this end, we propose a novel compact
deep sonar descriptor pipeline that can generalize to real scenarios while
being trained exclusively on synthetic data. Our architecture is based on a
ResNet18 back-end and a properly parameterized random Gaussian projection
layer, whereas input sonar data is enhanced with standard ad-hoc
normalization/prefiltering techniques. A customized synthetic data generation
procedure is also presented. The proposed method has been evaluated extensively
using both synthetic and publicly available real data, demonstrating its
effectiveness compared to state-of-the-art methods.Comment: This paper has been accepted for publication at the 14th
International Conference on Computer Vision Systems (ICVS 2023
Pushing the Limits of Learning-based Traversability Analysis for Autonomous Driving on CPU
Self-driving vehicles and autonomous ground robots require a reliable and
accurate method to analyze the traversability of the surrounding environment
for safe navigation. This paper proposes and evaluates a real-time machine
learning-based Traversability Analysis method that combines geometric features
with appearance-based features in a hybrid approach based on a SVM classifier.
In particular, we show that integrating a new set of geometric and visual
features and focusing on important implementation details enables a noticeable
boost in performance and reliability. The proposed approach has been compared
with state-of-the-art Deep Learning approaches on a public dataset of outdoor
driving scenarios. It reaches an accuracy of 89.2% in scenarios of varying
complexity, demonstrating its effectiveness and robustness. The method runs
fully on CPU and reaches comparable results with respect to the other methods,
operates faster, and requires fewer hardware resources.Comment: Accepted to 17th International Conference on Intelligent Autonomous
Systems (IAS-17