Search Results for author:
Found 10 papers, 2 papers with code
RoadRunner - Learning Traversability Estimation for Autonomous Off-road Driving
Furthermore, RoadRunner improves the system latency by a factor of roughly 4, from 500 ms to 140 ms, while improving the accuracy for traversability costs and elevation map predictions.
Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation
We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain using real-world offroad driving data.
LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigation
In previous work, we developed crater detection algorithms for three different sensing modalities.
LiDAR-guided object search and detection in Subterranean Environments
Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation.
Learning-based Localizability Estimation for Robust LiDAR Localization
LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time.
Self-supervised Learning of LiDAR Odometry for Robotic Applications
Reliable robot pose estimation is a key building block of many robot autonomy pipelines, with LiDAR localization being an active research domain.
Robotics
Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments
This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments.
Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments
Visible spectrum cameras are the most commonly used sensors due to their low cost and weight.
Keyframe-based Direct Thermal-Inertial Odometry
This paper proposes an approach for fusing direct radiometric data from a thermal camera with inertial measurements to extend the robotic capabilities of aerial robots for navigation in GPS-denied and visually degraded environments in the conditions of darkness and in the presence of airborne obscurants such as dust, fog and smoke.
Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments
For robotic inspection tasks in known environments fiducial markers provide a reliable and low-cost solution for robot localization.
