Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition

2024-10-03

arXiv.org

AI Summary

To address poor real-time loop closure detection in resource-constrained mobile robots performing large-scale, long-duration tasks—caused by LiDAR keyframe redundancy—this paper proposes a descriptor-feature-space-oriented keyframe sampling method. Innovatively introducing the concept of a “sampling space,” it shifts keyframe selection from the original 3D geometric space to a high-dimensional descriptor space. A parameter-free sliding-window optimization mechanism, integrated with feature-space density-aware sampling, enables adaptive, real-time keyframe selection across indoor and outdoor environments. The method is agnostic to descriptor type, supporting both learned and handcrafted descriptors within a unified framework. Experiments demonstrate substantial reductions in loop closure detection latency and memory footprint, robust performance across heterogeneous multi-source datasets, and seamless cross-scenario deployment without manual parameter tuning.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Dimensionality Reduction/Feature Selection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Recent advances in robotics are driving real-world autonomy for long-term and large-scale missions, where loop closures via place recognition are vital for mitigating pose estimation drift. However, achieving real-time performance remains challenging for resource-constrained mobile robots and multi-robot systems due to the computational burden of high-density sampling, which increases the complexity of comparing and verifying query samples against a growing map database. Conventional methods often retain redundant information or miss critical data by relying on fixed sampling intervals or operating in 3-D space instead of the descriptor feature space. To address these challenges, we introduce the concept of sample space and propose a novel keyframe sampling approach for LiDAR-based place recognition. Our method minimizes redundancy while preserving essential information in the hyper-dimensional descriptor space, supporting both learning-based and handcrafted descriptors. The proposed approach incorporates a sliding window optimization strategy to ensure efficient keyframe selection and real-time performance, enabling seamless integration into robotic pipelines. In sum, our approach demonstrates robust performance across diverse datasets, with the ability to adapt seamlessly from indoor to outdoor scenarios without parameter tuning, reducing loop closure detection times and memory requirements.

Problem

• Optimizing keyframe sampling for LiDAR-based place recognition
• Reducing computational burden in real-time robotics applications
• Minimizing redundancy in hyper-dimensional descriptor space

Innovation

• Keyframe sampling optimization
• Hyper-dimensional descriptor space
• Sliding window optimization strategy