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Found 10 papers, 2 papers with code
RMS: Redundancy-Minimizing Point Cloud Sampling for Real-Time Pose Estimation
The typical point cloud sampling methods used in state estimation for mobile robots preserve a high level of point redundancy.
Task-driven Compression for Collision Encoding based on Depth Images
This paper contributes a novel learning-based method for aggressive task-driven compression of depth images and their encoding as images tailored to collision prediction for robotic systems.
Visual-Thermal Camera Dataset Release and Multi-Modal Alignment without Calibration Information
This report accompanies a dataset release on visual and thermal camera data and details a procedure followed to align such multi-modal camera frames in order to provide pixel-level correspondence between the two without using intrinsic or extrinsic calibration information.
Anomalous Motion Detection on Highway Using Deep Learning
The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment.
Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?
Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years.
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.
Optical Flow Based Background Subtraction with a Moving Camera: Application to Autonomous Driving
In this research we present a novel algorithm for background subtraction using a moving camera.
