Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Skrzypczyński Piotr"'
Autor:
Banaszczyk, Adam, Łysakowski, Mikołaj, Nowicki, Michał R., Skrzypczyński, Piotr, Tadeja, Sławomir K.
Publikováno v:
2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
In this paper, we introduce a new methodology for assessing the positioning accuracy of virtual reality (VR) headsets, utilizing a cooperative industrial robot to simulate user head trajectories in a reproducible manner. We conduct a comprehensive ev
Externí odkaz:
http://arxiv.org/abs/2412.06116
Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8
Autor:
Łysakowski, Mikołaj, Żywanowski, Kamil, Banaszczyk, Adam, Nowicki, Michał R., Skrzypczyński, Piotr, Tadeja, Sławomir K.
This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment. Our approach uses the recent state-of-the-art YOLOv8 network that runs onboard on the Microsoft HoloLen
Externí odkaz:
http://arxiv.org/abs/2306.03537
Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence. Broadly speaking, reproducibility can be defined as the possibility to reproduce the same or a similar experiment or method, thereby obtaining t
Externí odkaz:
http://arxiv.org/abs/2302.12691
Autor:
Kicki, Piotr, Liu, Puze, Tateo, Davide, Bou-Ammar, Haitham, Walas, Krzysztof, Skrzypczyński, Piotr, Peters, Jan
Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed
Externí odkaz:
http://arxiv.org/abs/2301.04330
Although haptic sensing has recently been used for legged robot localization in extreme environments where a camera or LiDAR might fail, the problem of efficiently representing the haptic signatures in a learned prior map is still open. This paper in
Externí odkaz:
http://arxiv.org/abs/2209.15135
Autor:
Kicki, Piotr, Skrzypczyński, Piotr
This paper demonstrates how an efficient representation of the planned path using B-splines, and a construction procedure that takes advantage of the neural network's inductive bias, speed up both the inference and training of a DNN-based motion plan
Externí odkaz:
http://arxiv.org/abs/2203.06963
We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented that learns the visual context
Externí odkaz:
http://arxiv.org/abs/2108.01383
We propose a computationally efficient $G$-invariant neural network that approximates functions invariant to the action of a given permutation subgroup $G \leq S_n$ of the symmetric group on input data. The key element of the proposed network archite
Externí odkaz:
http://arxiv.org/abs/2012.06452
Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for ki
Externí odkaz:
http://arxiv.org/abs/2012.03707
An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic s
Externí odkaz:
http://arxiv.org/abs/2003.00946