A Comprehensive Survey on Global LiDAR Localization: Challenges, Advances, and Open Problems

Core Concepts

Knowledge about own pose is crucial for mobile robot applications, with LiDAR scanners being the standard sensor for localization and mapping.

Abstract

This article provides an overview of recent progress in LiDAR-based global localization. It covers key themes such as place retrieval, sequential global localization, and cross-robot localization. The content is organized under three main themes: maps for global localization, single-shot global localization focusing on place recognition and pose estimation, and methods for local transformation estimation. Various approaches are discussed, including dense points or voxels-based methods, projection-based techniques, and segmentation-based approaches. The article also delves into the challenges of feature extraction and robust estimators for point cloud registration.

Stats

Over the last two decades, LiDAR scanners have become the standard sensor for robot localization and mapping. The article discusses various methods for global LiDAR localization based on different types of maps. Place recognition-only approaches focus on retrieving places in a pre-built keyframe-based map. Local pose estimation methods aim to achieve high-precision transformation estimation through point cloud registration.

Quotes

Key Insights Distilled From

A Survey on Global LiDAR Localization

by Huan Yin,Xue... at arxiv.org 03-25-2024

https://arxiv.org/pdf/2302.07433.pdf

Deeper Inquiries

How can advancements in deep learning improve feature extraction from 3D point clouds?

Advancements in deep learning have significantly improved feature extraction from 3D point clouds by enabling the development of more robust and efficient algorithms. Deep learning techniques, such as convolutional neural networks (CNNs) and PointNet, have shown great potential in extracting meaningful features from raw 3D data. These models can automatically learn hierarchical representations of point cloud data, capturing both local and global geometric patterns. Point Cloud Processing: Deep learning models like PointNet and its variants can directly process raw point cloud data without the need for manual feature engineering. They are capable of capturing complex spatial relationships within the point cloud, leading to more informative feature representations. Local Feature Extraction: CNN-based architectures have been successful in extracting local features from individual points or small neighborhoods within a point cloud. By leveraging shared weights and hierarchical structures, these models can effectively capture intricate details at different scales. Global Context Integration: Deep learning approaches allow for the integration of global context information into feature extraction processes. This enables a better understanding of the overall structure of the point cloud, leading to more discriminative features that are invariant to transformations like rotation or translation. Robustness to Noise: Deep learning models can be trained on large-scale datasets with varying levels of noise and outliers, making them inherently robust to noisy input data during feature extraction tasks. End-to-End Learning: With end-to-end training pipelines, deep learning models can optimize feature extraction processes along with downstream tasks like registration or classification, resulting in improved performance across multiple stages of processing.

How might advancements in deep learning impact keypoint detection in 3D point clouds?

Advancements in deep learning have the potential to revolutionize keypoint detection in 3D point clouds by offering more sophisticated methods for identifying stable keypoints that are invariant under transformations: Unsupervised Frameworks: The integration of unsupervised frameworks could lead to novel approaches for detecting keypoints based on intrinsic properties within 3D structures rather than relying solely on handcrafted rules or supervision. Feature Learning: Deep learning techniques enable end-to-end training pipelines that learn discriminative features directly from raw data without explicit human intervention. This capability could enhance keypoint detection by automatically discovering relevant patterns and structures within 3D point clouds. 3Improved Generalization: Advanced neural network architectures equipped with regularization techniques could improve generalization abilities when detecting keypoints across diverse environments or datasets not encountered during training sessions. 4Semantic Information Utilization: Incorporating semantic information into keypoint detection through deep neural networks may help identify salient regions based on contextual understanding rather than purely geometric considerations. 5Efficient Keypoint Detection: Efficient algorithms utilizing sparse convolutions or attention mechanisms could streamline keypoint detection processes while maintaining accuracy even on large-scale datasets containing millions of points.