A Framework for Reproducible Benchmarking and Performance Diagnosis of SLAM Systems

TL;DR

SLAMFuse is an open-source benchmarking framework that enables consistent, reproducible evaluation and diagnosis of SLAM algorithms across multiple datasets and conditions using Docker-based tools and dataset fuzzing.

Contribution

It introduces a comprehensive framework with dataset fuzzing, failure detection, and diagnosis tools for reproducible SLAM benchmarking across diverse environments.

Findings

Fuzzing mechanism tests SLAM resilience to dataset perturbations.

Failure detection improves diagnosis of SLAM algorithm failures.

Docker ensures reproducibility across platforms.

Abstract

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