Search Results for author: Shehryar Khattak

Found 12 papers, 3 papers with code

Enabling Novel Mission Operations and Interactions with ROSA: The Robot Operating System Agent

1 code implementation9 Oct 2024 Rob Royce, Marcel Kaufmann, Jonathan Becktor, Sangwoo Moon, Kalind Carpenter, Kai Pak, Amanda Towler, Rohan Thakker, Shehryar Khattak

The advancement of robotic systems has revolutionized numerous industries, yet their operation often demands specialized technical knowledge, limiting accessibility for non-expert users.

RoadRunner M&M -- Learning Multi-range Multi-resolution Traversability Maps for Autonomous Off-road Navigation

no code implementations17 Sep 2024 Manthan Patel, Jonas Frey, Deegan Atha, Patrick Spieler, Marco Hutter, Shehryar Khattak

RoadRunner M&M achieves a significant improvement of up to 50% for elevation mapping and 30% for traversability estimation over RoadRunner, and is able to predict in 30% more regions compared to X-Racer while achieving real-time performance.

Robot Navigation

RoadRunner -- Learning Traversability Estimation for Autonomous Off-road Driving

no code implementations29 Feb 2024 Jonas Frey, Manthan Patel, Deegan Atha, Julian Nubert, David Fan, Ali Agha, Curtis Padgett, Patrick Spieler, Marco Hutter, Shehryar Khattak

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.

Autonomous Driving Autonomous Navigation +1

Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation

no code implementations30 Jan 2024 Chanyoung Chung, Georgios Georgakis, Patrick Spieler, Curtis Padgett, Ali Agha, Shehryar Khattak

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.

LiDAR-guided object search and detection in Subterranean Environments

no code implementations26 Oct 2022 Manthan Patel, Gabriel Waibel, Shehryar Khattak, Marco Hutter

Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation.

Disaster Response object-detection +1

Learning-based Localizability Estimation for Robust LiDAR Localization

1 code implementation11 Mar 2022 Julian Nubert, Etienne Walther, Shehryar Khattak, Marco Hutter

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.

Motion Estimation

Self-supervised Learning of LiDAR Odometry for Robotic Applications

1 code implementation10 Nov 2020 Julian Nubert, Shehryar Khattak, Marco Hutter

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

no code implementations5 Mar 2019 Shehryar Khattak, Christos Papachristos, Kostas Alexis

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.

Keyframe-based Direct Thermal-Inertial Odometry

no code implementations3 Mar 2019 Shehryar Khattak, Christos Papachristos, Kostas Alexis

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

no code implementations2 Mar 2019 Shehryar Khattak, Christos Papachristos, Kostas Alexis

For robotic inspection tasks in known environments fiducial markers provide a reliable and low-cost solution for robot localization.

Navigate Pose Estimation

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