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2012.02111
Cited By

Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping

Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping

IEEE/RJS International Conference on Intelligent RObots and Systems (IROS), 2020

2 December 2020

ArXiv (abs)PDF HTML

Papers citing "Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping"

4 / 4 papers shown

Deep Learning-Driven State Correction: A Hybrid Architecture for
Radar-Based Dynamic Occupancy Grid Mapping

Deep Learning-Driven State Correction: A Hybrid Architecture for Radar-Based Dynamic Occupancy Grid Mapping

Daniel Watzenig

196

5

0

22 May 2024

Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Maps

Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Maps

138

3

0

21 May 2023

Deformable Radar Polygon: A Lightweight and Predictable Occupancy
Representation for Short-range Collision Avoidance

Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision AvoidanceIEEE Sensors Journal (IEEE Sens. J.), 2022

Harshavardhan Reddy Dasari

332

8

0

02 Mar 2022

A Simulation-based End-to-End Learning Framework for Evidential
Occupancy Grid Mapping

A Simulation-based End-to-End Learning Framework for Evidential Occupancy Grid Mapping

Raphael van Kempen

217

14

0

25 Feb 2021

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