构建可复现的SLAM评估框架，自动检测算法失效并诊断性能瓶颈。
A Framework for Reproducible Benchmarking and Performance Diagnosis of SLAM Systems
- 基于Docker的跨平台环境，统一依赖管理确保测试一致。
- 引入数据扰动机制，量化算法在不同干扰下的鲁棒性阈值。
- 提供故障检测与分析工具，适合算法开发者和评测研究人员。
我们提出SLAMFuse，一个开源的SLAM基准测试框架，提供跨平台一致的多模态SLAM算法评估环境，并包含数据扰动、故障检测与诊断工具。该框架通过扰动机制测试SLAM算法对数据偏差的鲁棒性，评估姿态估计精度在不同条件下的表现，并识别关键扰动阈值。借助Docker实现依赖项的统一管理，保障在多种数据集与系统间的可复现性。框架还提供故障检测与行为分析工具，帮助理解算法对数据特征的响应。我们公开了实验中使用的算法与数据集的Docker兼容版本，以及新算法集成与评测指南。代码已发布于https://github.com/nikolaradulov/slamfuse。
原文摘要 · Abstract (English)
We propose SLAMFuse, an open-source SLAM benchmarking framework that provides consistent crossplatform environments for evaluating multi-modal SLAM algorithms, along with tools for data fuzzing, failure detection, and diagnosis across different datasets. Our framework introduces a fuzzing mechanism to test the resilience of SLAM algorithms against dataset perturbations. This enables the assessment of pose estimation accuracy under varying conditions and identifies critical perturbation thresholds. SLAMFuse improves diagnostics with failure detection and analysis tools, examining algorithm behaviour against dataset characteristics. SLAMFuse uses Docker to ensure reproducible testing conditions across diverse datasets and systems by streamlining dependency management. Emphasizing the importance of reproducibility and introducing advanced tools for algorithm evaluation and performance diagnosis, our work sets a new precedent for reliable benchmarking of SLAM systems. We provide ready-to-use docker compatible versions of the algorithms and datasets used in the experiments, together with guidelines for integrating and benchmarking new algorithms. Code is available at https://github.com/nikolaradulov/slamfuse
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