Search Results for author: Piotr Skrzypczyński

Found 7 papers, 6 papers with code

Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction

1 code implementation14 Mar 2022 Piotr Kicki, Piotr Skrzypczyński

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 planner.

Inductive Bias

On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM

1 code implementation3 Aug 2021 Jan Wietrzykowski, Piotr Skrzypczyński

We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments.

Loop Closure Detection

A New Neural Network Architecture Invariant to the Action of Symmetry Subgroups

1 code implementation11 Dec 2020 Piotr Kicki, Mete Ozay, Piotr Skrzypczyński

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.

Learning from Experience for Rapid Generation of Local Car Maneuvers

1 code implementation7 Dec 2020 Piotr Kicki, Tomasz Gawron, Krzysztof Ćwian, Mete Ozay, Piotr Skrzypczyński

Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy.

A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment

1 code implementation2 Mar 2020 Piotr Kicki, Tomasz Gawron, Piotr Skrzypczyński

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 scenarios are highly dynamic.

Self-Supervised Learning

A Computationally Efficient Neural Network Invariant to the Action of Symmetry Subgroups

no code implementations18 Feb 2020 Piotr Kicki, Mete Ozay, Piotr Skrzypczyński

The key element of the proposed network architecture is a new $G$-invariant transformation module, which produces a $G$-invariant latent representation of the input data.

Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

2 code implementations7 Nov 2016 Michał Nowicki, Jan Wietrzykowski, Piotr Skrzypczyński

The paper presents an approach to indoor personal localization on a mobile device based on visual place recognition.

Visual Place Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.