Search Results for author:
Found 6 papers, 5 papers with code
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty
An agent can use domain knowledge provided by human experts to learn efficiently.
Robust Place Recognition using an Imaging Lidar
We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds.
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building.
Robotics
Simulation-based Lidar Super-resolution for Ground Vehicles
We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar.
Robotics Image and Video Processing
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning
Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy.
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain
We propose a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles.
