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Found 7 papers, 6 papers with code
Speeding up deep neural network-based planning of local car maneuvers via efficient B-spline path construction
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.
On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM
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 Neural Network Architecture Invariant to the Action of Symmetry Subgroups
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
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
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.
A Computationally Efficient Neural Network Invariant to the Action of Symmetry Subgroups
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
The paper presents an approach to indoor personal localization on a mobile device based on visual place recognition.
