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An open-source navigation system for micro aerial vehicles

Ivan Dryanovski, Roberto G. Valenti, Jizhong Xiao
2013 Autonomous Robots  
This paper presents an open-source indoor navigation system for quadrotor micro aerial vehicles(MAVs), implemented in the ROS framework. The system requires a minimal set of sensors including a planar laser range-finder and an inertial measurement unit. We address the issues of autonomous control, state estimation, path-planning, and teleoperation, and provide interfaces that allow the system to seamlessly integrate with existing ROS navigation tools for 2D SLAM and 3D mapping. All components
more » ... n in real time onboard the MAV, with state estimation and control operating at 1 kHz. A major focus in our work is modularity and abstraction, allowing the system to be both flexible and hardware-independent. All the software and hardware components which we have developed, as well as documentation and test data, are available online.
doi:10.1007/s10514-012-9318-8 fatcat:cwh5qojkgnbr7lhwlc2eowv65y

Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs

Roberto Valenti, Ivan Dryanovski, Jizhong Xiao
2015 Sensors  
Valenti and Ivan Dryanovski conceived the mathematical derivation of the presented approach and wrote the implementation of the algorithm; Roberto G.  ... 
doi:10.3390/s150819302 pmid:26258778 pmcid:PMC4570372 fatcat:y77ugzn465a4didxuyscf53yym

Semantic Indoor Navigation with a Blind-User Oriented Augmented Reality

Samleo L. Joseph, Xiaochen Zhang, Ivan Dryanovski, Jizhong Xiao, Chucai Yi, YingLi Tian
2013 2013 IEEE International Conference on Systems, Man, and Cybernetics  
The aim of this paper is to design an inexpensive conceivable wearable navigation system that can aid in the navigation of a visually impaired user. A novel approach of utilizing the floor plan map posted on the buildings is used to acquire a semantic plan. The extracted landmarks such as room numbers, doors, etc act as a parameter to infer the way points to each room. This provides a mental mapping of the environment to design a navigation framework for future use. A human motion model is used
more » ... to predict a path based on how real humans ambulate towards a goal by avoiding obstacles. We demonstrate the possibilities of augmented reality (AR) as a blind user interface to perceive the physical constraints of the real world using haptic and voice augmentation. The haptic belt vibrates to direct the user towards the travel destination based on the metric localization at each step. Moreover, travel route is presented using voice guidance, which is achieved by accurate estimation of the user's location and confirmed by extracting the landmarks, based on landmark localization. The results show that it is feasible to assist a blind user to travel independently by providing the constraints required for safe navigation with user oriented augmented reality.
doi:10.1109/smc.2013.611 dblp:conf/smc/JosephZDXYT13 fatcat:uzkbpx75nfbzxo7klagza2wbuq

Chisel: Real Time Large Scale 3D Reconstruction Onboard a Mobile Device using Spatially Hashed Signed Distance Fields

Matthew Klingensmith, Ivan Dryanovski, Siddhartha Srinivasa, Jizhong Xiao
2015 Robotics: Science and Systems XI  
We describe CHISEL: a system for real-time housescale (300 square meter or more) dense 3D reconstruction onboard a Google Tango [1] mobile device by using a dynamic spatially-hashed truncated signed distance field[2] for mapping, and visual-inertial odometry for localization. By aggressively culling parts of the scene that do not contain surfaces, we avoid needless computation and wasted memory. Even under very noisy conditions, we produce high-quality reconstructions through the use of space
more » ... rving. We are able to reconstruct and render very large scenes at a resolution of 2-3 cm in real time on a mobile device without the use of GPU computing. The user is able to view and interact with the reconstruction in real-time through an intuitive interface. We provide both qualitative and quantitative results on publicly available RGB-D datasets [3] , and on datasets collected in real-time from two devices.
doi:10.15607/rss.2015.xi.040 dblp:conf/rss/KlingensmithDSX15 fatcat:pvjjptcp3jgphict4amakwjcwe

Editor's note: Special Issue on Robotics: Science and Systems, 2015

2017 Autonomous Robots  
Jingru Luo, Kris Hauser • Autonomy infused teleoperation with application to brain Large-scale, real-time 3D scene reconstruction on a mobile device, Ivan Dryanovski, Matthew Klingensmith, Siddhartha S  ... 
doi:10.1007/s10514-017-9647-8 fatcat:xmdpgbob6rdcbkqqu7ew2t5baq

TSDF-based change detection for consistent long-term dense reconstruction and dynamic object discovery

Marius Fehr, Fadri Furrer, Ivan Dryanovski, Jürgen Sturm, Igor Gilitschenski, Roland Siegwart, Cesar Cadena
2017
Fig. 1: Change Detection Algorithm: (left) One of 10 reconstructed scene observations. (center) Reconstruction of the static environment after 10 observations. (right) Discovered dynamic objects. Abstract-Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extended Truncated Signed Distance Function (TSDF) that
more » ... to continuously refine the static map while simultaneously obtaining 3D reconstructions of dynamic objects in the scene. This is a challenging problem because map updates happen incrementally and are often incomplete. Previous work typically performs change detection on point clouds, surfels or maps, which are not able to distinguish between unexplored and empty space. In contrast, our TSDF-based representation naturally contains this information and thus allows us to more robustly solve the scene differencing problem. We demonstrate the algorithms performance as part of a system for unsupervised object discovery and class recognition. We evaluated our algorithm on challenging datasets that we recorded over several days with RGB-D enabled tablets. To stimulate further research in this area, all of our datasets are publicly available 3 .
doi:10.3929/ethz-b-000189737 fatcat:hhmzduwsvzfypjhd7qzqg74ri4

Visual-Inertial Teach and Repeat for Aerial Inspection [article]

Marius Fehr, Thomas Schneider, Marcin Dymczyk, Jürgen Sturm, and Roland Siegwart
2018 arXiv   pre-print
Dryanovski, Simon Lynen, and Konstantine Tsotsos.  ...  presented experiments received support from members of the Autonomous Systems Lab and Google Tango, most importantly: Michael Burri, Helen Oleynikova, Zachary Taylor, Fabian Blöchliger, Mingyang Li, Ivan  ... 
arXiv:1803.09650v1 fatcat:46urxwwabfe4xgwqb6nbhs6t4q

A Humanoid Robot Companion for Wheelchair Users [chapter]

Miguel Sarabia, Yiannis Demiris
2013 Lecture Notes in Computer Science  
Node written by Ivan Dryanovski and William Morris, available from http://www.ros.org/wiki/laser scan matcher. 5 Node written by Brian Gerkey and Andrew Howards, available from http://www.ros.org/wiki  ... 
doi:10.1007/978-3-319-02675-6_43 fatcat:r5zorl22kreytni3yaz4njp6pa

Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis

Angela Dai, Charles Ruizhongtai Qi, Matthias NieBner
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We want to thank Ivan Dryanovski and Jürgen Sturm for their valuable feedback and help during this project, and Wenzel Jakob for the Mitsuba raytracer [13] . [2] .  ... 
doi:10.1109/cvpr.2017.693 dblp:conf/cvpr/DaiQN17 fatcat:th4cmo4jlnh4fpe4ilcbdm6hsq

Building Optimal Radio-Frequency Signal Maps

Piotr Mirowski, Tin Kam Ho, Philip Whiting
2014 2014 22nd International Conference on Pattern Recognition  
ACKNOWLEDGEMENTS The authors would like to acknowledge the helpful contributions by Ravishankar Palaniappan, Ivan Dryanovski and Hao Tong during the experimental (data acquisition) phase.  ... 
doi:10.1109/icpr.2014.178 dblp:conf/icpr/MirowskiHW14 fatcat:bfpdwlmmqfbjtaaniqn2amsh7u

A flight altitude estimator for multirotor UAVs in dynamic and unstructured indoor environments

Hriday Bavle, Jose Luis Sanchez-Lopez, Alejandro Rodriguez-Ramos, Carlos Sampedro, Pascual Campoy
2017 2017 International Conference on Unmanned Aircraft Systems (ICUAS)  
Ivan Dryanovski et al. [16] proposes an approach for robust altitude estimation of the UAV by deflecting the rays from a horizontally mounted laser to the ground using a mirror.  ... 
doi:10.1109/icuas.2017.7991467 fatcat:fvgjdsixjjfr3duymxh2rfp24u

A Wearable Indoor Navigation System with Context Based Decision Making For Visually Impaired

Xiaochen Zhang
2016 International Journal of Advanced Robotics and Automation  
Ivan Dryanovski for his works on visual odometry. Prof.  ... 
doi:10.15226/2473-3032/1/3/00115 fatcat:uaixslfwdzbr3jleasdf4rx65u

Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis [article]

Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner
2017 arXiv   pre-print
We want to thank Ivan Dryanovski and Jürgen Sturm for their valuable feedback and help during this project, and Wenzel Jakob for the Mitsuba raytracer [15] .  ... 
arXiv:1612.00101v2 fatcat:j2y5z5hv6vbypf767eu2kbrzj4

Learning Shared Control by Demonstration for Personalized Wheelchair Assistance

Ayse Kucukyilmaz, Yiannis Demiris
2018 IEEE Transactions on Haptics  
Software, written by Ivan Dryanovski and William Morris, is available from http://www.ros.org/wiki/laser scan matcher.3.  ... 
doi:10.1109/toh.2018.2804911 pmid:29994370 fatcat:gm5qv7ugtzdnbi4ie474qfpvwq

NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video [article]

Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, Hujun Bao
2021 arXiv   pre-print
Barron, Neal Wadhwa, Max Dzitsiuk, Michael Schoenberg, Vivek Verma, Ambrus Csaszar, Eric Turner, Ivan Dryanovski, Joao Afonso, Jose Pascoal, Konstantine Tsotsos, Mira Leung, Mirko Schmidt  ... 
arXiv:2104.00681v1 fatcat:gsuhqfhiirbhdeelqolnbwn6hu
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