hridaybavle/ir-mcl

IR-MCL: Implicit Representation-Based Online Global Localization https://arxiv.org/abs/2210.03113

IR-MCL: Implicit Representation-Based Online Global Localization

This repo contains the code of the paper: IR-MCL: Implicit Representation-Based Online Global Localization,

by Haofei Kuang, Xieyuanli Chen, Tiziano Guadagnino, Nicky Zimmerman, Jens Behley and Cyrill Stachniss

Arxiv Preprint

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IR-MCL pipeline

Online localization demo

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.

Code

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).