Search Results for author: Jen Jen Chung

Found 15 papers, 9 papers with code

Baking in the Feature: Accelerating Volumetric Segmentation by Rendering Feature Maps

no code implementations26 Sep 2022 Kenneth Blomqvist, Lionel Ott, Jen Jen Chung, Roland Siegwart

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

1 code implementation23 Mar 2022 Daniel Dugas, Olov Andersson, Roland Siegwart, Jen Jen Chung

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.

Atari Games Navigate

Descriptellation: Deep Learned Constellation Descriptors

no code implementations1 Mar 2022 Chunwei Xing, Xinyu Sun, Andrei Cramariuc, Samuel Gull, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Florian Tschopp

However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.

Simultaneous Localization and Mapping

Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses

1 code implementation19 Jan 2022 Kenneth Blomqvist, Jen Jen Chung, Lionel Ott, Roland Siegwart

In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation.

Object Tracking Pose Estimation

Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter

1 code implementation4 Jan 2021 Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, Juan Nieto

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.


Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning

1 code implementation4 Nov 2020 Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer, Jen Jen Chung, Roland Siegwart, Cesar Cadena

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.

reinforcement-learning Reinforcement Learning (RL)

With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

no code implementations25 Sep 2020 Álvaro Serra-Gómez, Bruno Brito, Hai Zhu, Jen Jen Chung, Javier Alonso-Mora

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.

Motion Planning

Go Fetch: Mobile Manipulation in Unstructured Environments

no code implementations2 Apr 2020 Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc, Julian Förster, Margarita Grinvald, Florian Tschopp, Jen Jen Chung, Lionel Ott, Juan Nieto, Roland Siegwart

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.

Motion Planning

Revisiting Boustrophedon Coverage Path Planning as a Generalized Traveling Salesman Problem

1 code implementation22 Jul 2019 Rik Bähnemann, Nicholas Lawrance, Jen Jen Chung, Michael Pantic, Roland Siegwart, Juan Nieto

In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs).


Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery

1 code implementation IEEE ROBOTICS AND AUTOMATION LETTERS 2019 Margarita Grinvald, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Juan Nieto

To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes.


An informative path planning framework for UAV-based terrain monitoring

1 code implementation8 Sep 2018 Marija Popovic, Teresa Vidal-Calleja, Gregory Hitz, Jen Jen Chung, Inkyu Sa, Roland Siegwart, Juan Nieto

Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications.


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