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Found 8 papers, 3 papers with code
Joint Spatial-Temporal Calibration for Camera and Global Pose Sensor
Yet, to work optimally, these functionalities require having accurate and reliable spatial-temporal calibration parameters between the camera and the global pose sensor.
Room impulse response reconstruction with physics-informed deep learning
A method is presented for estimating and reconstructing the sound field within a room using physics-informed neural networks.
DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories
This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments.
OmniLRS: A Photorealistic Simulator for Lunar Robotics
Trained on our synthetic data, a yolov8 model achieves performance close to a model trained on real-world data, with 5% performance gap.
How To Train Your HERON
In this paper we apply Deep Reinforcement Learning (Deep RL) and Domain Randomization to solve a navigation task in a natural environment relying solely on a 2D laser scanner.
A Study on Trees's Knots Prediction from their Bark Outer-Shape
In the industry, the value of wood-logs strongly depends on their internal structure and more specifically on the knots' distribution inside the trees.
A Survey On 3D Inner Structure Prediction from its Outer Shape
Our goal is to design neural-network-based methods to predict the internal density of the tree from its external bark shape.
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry
The core of our approach is a semantic nearest neighbor field that facilitates a robust data association of edges across frames using semantics.
