From a874e7a4d8a65dae7756ee19f469add9aca82d44 Mon Sep 17 00:00:00 2001 From: Kuang Haofei <haofeikuang@gmail.com> Date: Wed, 5 Oct 2022 18:30:35 +0200 Subject: [PATCH] Update README.md add figures --- README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 68fcc44..df22906 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,16 @@ This repo contains the code of the paper: by [Haofei Kuang](https://www.ipb.uni-bonn.de/people/haofei-kuang/), [Xieyuanli Chen](https://www.ipb.uni-bonn.de/people/xieyuanli-chen/), [Tiziano Guadagnino](https://phd.uniroma1.it/web/TIZIANO-GUADAGNINO_nP1536210_IT.aspx), [Nicky Zimmerman](https://www.ipb.uni-bonn.de/people/nicky-zimmerman/), [Jens Behley](https://www.ipb.uni-bonn.de/people/jens-behley/) and [Cyrill Stachniss](https://www.ipb.uni-bonn.de/people/cyrill-stachniss/) ---- +### IR-MCL pipeline. +<p align="center"> +<img src="https://user-images.githubusercontent.com/18661888/194111809-4f966ab5-64be-45fc-963b-a6fe0a8c14ed.png" width="700"/> +</p> + +### Online localization demo +<p align="center"> +<img src="https://user-images.githubusercontent.com/18661888/194112420-f83c2d02-e33b-4e8f-87df-bcaab12641a2.gif" width="800"> +</p> + ## Abstract Determining the state of a mobile robot is an essential building block of robot navigation systems. In this paper, we address the problem of estimating the robot’s pose in an indoor environment using 2D LiDAR data and investigate how modern environment models can improve gold standard Monte-Carlo localization (MCL) systems. We propose a neural occupancy field (NOF) to implicitly represent the scene using a neural network. With the pretrained network, we can synthesize 2D LiDAR scans for an arbitrary robot pose through volume rendering. Based on the implicit representation, we can obtain the similarity between a synthesized and actual scan as an observation model and integrate it into an MCL system to perform accurate localization. We evaluate our approach on five sequences of a self-recorded dataset and three publicly available datasets. We show that we can accurately and efficiently localize a robot using our approach surpassing the localization performance of state-of-the-art methods. The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization. @@ -14,4 +24,4 @@ Coming soon! ## Acknowledgment This work has partially been funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement -No 101017008 (Harmony). \ No newline at end of file +No 101017008 (Harmony).