Search Results for author:
Found 5 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.
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.
Lessons from a Space Lab -- An Image Acquisition Perspective
From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection.
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning
Domain randomization improves the generalization of learned policies to novel scenes with previously unseen objects and different illumination conditions.
