# IR-MCL: Implicit Representation-Based Online Global Localization arXiv: https://arxiv.org/abs/2210.03113 ยท HTML: https://mlsanity.com/paper/2210.03113/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 50 references that are in the map. - First posted on arXiv 6 Oct 2022. ## Builds on (most cited first, 27 of 27) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019) - [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020) - [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021) - [UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction](https://mlsanity.com/paper/2104.10078/) (2021) - [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021) - [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021) - [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021) - [INeRF: Inverting Neural Radiance Fields for Pose Estimation](https://mlsanity.com/paper/2012.05877/) (2020) - [Dense Depth Priors for Neural Radiance Fields from Sparse Input Views](https://mlsanity.com/paper/2112.03288/) (2021) - [Urban Radiance Fields](https://mlsanity.com/paper/2111.14643/) (2021) - [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021) - [MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis](https://mlsanity.com/paper/2103.14910/) (2021) - [LENS: Localization enhanced by NeRF synthesis](https://mlsanity.com/paper/2110.06558/) (2021) - [iSDF: Real-Time Neural Signed Distance Fields for Robot Perception](https://mlsanity.com/paper/2204.02296/) (2022) - [NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields](https://mlsanity.com/paper/2203.01913/) (2022) - [Loc-NeRF: Monte Carlo Localization using Neural Radiance Fields](https://mlsanity.com/paper/2209.09050/) (2022) - [Generative Occupancy Fields for 3D Surface-Aware Image Synthesis](https://mlsanity.com/paper/2111.00969/) (2021) - [Reinforcement Learning with Neural Radiance Fields](https://mlsanity.com/paper/2206.01634/) (2022) - [Continual Neural Mapping: Learning An Implicit Scene Representation from Sequential Observations](https://mlsanity.com/paper/2108.05851/) (2021) - [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021) - [LASER: LAtent SpacE Rendering for 2D Visual Localization](https://mlsanity.com/paper/2204.00157/) (2022) - [Learning an Overlap-based Observation Model for 3D LiDAR Localization](https://mlsanity.com/paper/2105.11717/) (2021) - [Robust Onboard Localization in Changing Environments Exploiting Text Spotting](https://mlsanity.com/paper/2203.12647/) (2022) - [Fast Gaussian Process Occupancy Maps](https://mlsanity.com/paper/1811.10156/) (2018) ## Built on by (most cited first, 14 of 14) - [PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency](https://mlsanity.com/paper/2401.09101/) (2024) - [Neural Radiance Field in Autonomous Driving: A Survey](https://mlsanity.com/paper/2404.13816/) (2024) - [NeRFs in Robotics: A Survey](https://mlsanity.com/paper/2405.01333/) (2024) - [PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames in Autonomous Driving Environments](https://mlsanity.com/paper/2402.09325/) (2024) - [PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments](https://mlsanity.com/paper/2310.00874/) (2023) - [Towards Real-Time Neural Volumetric Rendering on Mobile Devices: A Measurement Study](https://mlsanity.com/paper/2406.16068/) (2024) - [UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation](https://mlsanity.com/paper/2503.21338/) (2025) - [ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors](https://mlsanity.com/paper/2512.09065/) (2025) - [CCNDF: Curvature Constrained Neural Distance Fields from 3D LiDAR Sequences](https://mlsanity.com/paper/2412.15909/) (2024) - [BIM-Constrained Optimization for Accurate Localization and Deviation Correction in Construction Monitoring](https://mlsanity.com/paper/2504.17693/) (2025) - [HiLO: High-Level Object Fusion for Autonomous Driving using Transformers](https://mlsanity.com/paper/2506.02554/) (2025) - [Multi-Representation Geometric Hierarchy Fusion: An Implicit-Submap Driven Framework for Resilient 3D Place Recognition](https://mlsanity.com/paper/2506.14243/) (2025) - [GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology](https://mlsanity.com/paper/2604.15495/) (2026) - [VLM-GLoc: Vision-Language Model Enhanced Monte Carlo Localization for Robust Semantic Global Localization in Cluttered Quasi-Static Environments](https://mlsanity.com/paper/2605.30506/) (2026) ## Abstract (arXiv) 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 robots 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 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 self-recorded datasets and three publicly available ones. 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. The code of our approach will be available at: https://github.com/PRBonn/ir-mcl.