IR-MCL: Implicit Representation-Based Online Global Localization (2210.03113v2)

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 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.

• The paper introduces an implicit neural occupancy field to overcome limitations of explicit grid maps.
• It employs LiDAR ray casting and positional encoding within a Monte Carlo framework to synthesize accurate observations.
• IR-MCL outperforms traditional methods by significantly enhancing localization accuracy and efficiency in complex indoor environments.

Overview of "IR-MCL: Implicit Representation-Based Online Global Localization"

This paper presents a novel approach to robot localization, titled IR-MCL, which leverages implicit neural representations to enhance the performance of Monte-Carlo localization (MCL) systems using 2D LiDAR data. The proposed method introduces a neural occupancy field (NOF) to implicitly model the robot's environment. Unlike traditional explicit grid maps, the NOF enables more efficient and accurate localization by generalizing well to unseen environments.

Key Contributions

1. Implicit Environment Representation: The paper proposes using a neural network to create an implicit representation of the environment, which predicts occupancy probabilities for 2D locations. This approach is a significant shift from traditional occupancy grid maps that are limited by discrete cell representations.
2. Advanced Observation Modeling: The research outlines a method for synthesizing 2D LiDAR scans using volume rendering techniques. These synthesized observations can then be compared with actual sensor data to improve the observation model in localization tasks.
3. Enhanced Localization Performance: Compared to state-of-the-art methods, the IR-MCL system demonstrates superior accuracy in localizing mobile robots in indoor environments. This improvement is particularly evident in areas with intricate geometries that are not well-captured by conventional discrete maps.

Methodology

The approach involves training a neural network that takes a 2D position as input and outputs an occupancy probability, forming a NOF. The method uses positional encoding to allow the neural network to capture fine-grained details of the environment. A LiDAR-based ray casting rendering algorithm synthesizes virtual scans, which are then utilized in a particle filter framework to update the belief of the robot's pose effectively.

Experimental Evaluation

The method was tested using self-recorded datasets and publicly available benchmarks. The results showed that IR-MCL consistently outperformed existing MCL methods in terms of speed and localization accuracy. Notably, the system maintained robustness across different indoor scenes, owing to its ability to synthesize accurate 2D LiDAR measurements from the implicit representation.

Practical and Theoretical Implications

The introduction of implicit representations in robot localization offers a dual advantage: it reduces the memory footprint required for map storage and provides a continuous representation that improves pose estimation. These capabilities suggest potential applications beyond traditional indoor scenarios, including dynamic environments where adaptability and real-time processing are critical.

Future Directions

The paper hints at the potential of further integrating deep learning with localization and mapping, particularly involving environments with dynamic elements. Future research may explore extending implicit representations to 3D environments or incorporating multi-sensor data to enhance robustness and adaptability in diverse operational conditions.

In conclusion, IR-MCL represents a significant advancement in global localization methodologies, leveraging modern deep learning techniques to address limitations posed by traditional explicit mapping approaches. The work opens new avenues for efficient and accurate localization across a variety of challenging settings, underscoring the symbiotic relationship between learning-based approaches and robotic perception.