Search Results for author:
Found 18 papers, 12 papers with code
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models
Our method is based on a NeuS2 object representation, that we learn through a semi-automated procedure based on Structure-from-Motion (SfM) and object-agnostic segmentation.
WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV
Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation.
Neural Implicit Vision-Language Feature Fields
In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method.
Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels.
NavDreams: Towards Camera-Only RL Navigation Among Humans
In order to successfully solve the navigation task from only images, algorithms must be able to model the scene and its dynamics using only this channel of information.
Descriptellation: Deep Learned Constellation Descriptors
However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation.
Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene.
Robotics
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human Environments
In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation.
MultiPoint: Cross-spectral registration of thermal and optical aerial imagery
This model is then deployed for fast and accurate online interest point detection.
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
Witness Autoencoder: Shaping the Latent Space with Witness Complexes
We present a Witness Autoencoder (W-AE) – an autoencoder that captures geodesic distances of the data in the latent space.
With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance
Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots.
Go Fetch: Mobile Manipulation in Unstructured Environments
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements.
Revisiting Boustrophedon Coverage Path Planning as a Generalized Traveling Salesman Problem
In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs).
Robotics
Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery
To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes.
Robotics
An informative path planning framework for UAV-based terrain monitoring
Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications.
Robotics
