[Paper Review] Predicting Performance of SLAM Algorithms

Matteo Luperto, Valerio Castelli+1 more|arXiv (Cornell University)|Sep 6, 2021

Robotics and Sensor-Based Localization42 references4 citations

TL;DR

This paper proposes a predictive benchmarking method for SLAM algorithms that uses linear models trained on simulated environments to forecast performance in unseen real or simulated environments. By analyzing geometrical features—specifically the Voronoi graph of environments—it predicts localization errors with high accuracy, enabling design-time selection of optimal SLAM algorithms without prior deployment.

ABSTRACT

Among the abilities that autonomous mobile robots should exhibit, map building and localization are definitely recognized as fundamental. Consequently, countless algorithms for solving the Simultaneous Localization And Mapping (SLAM) problem have been proposed. Currently, their evaluation is performed ex-post, according to outcomes obtained when running the algorithms on data collected by robots in real or simulated environments. In this paper, we present a novel method that allows the ex-ante prediction of the performance of a SLAM algorithm in an unseen environment, before it is actually run. Our method collects the performance of a SLAM algorithm in a number of simulated environments, builds a model that represents the relationship between the observed performance and some geometrical features of the environments, and exploits such model to predict the performance of the algorithm in an unseen environment starting from its features.

Motivation & Objective

• To enable ex ante prediction of SLAM algorithm performance in new, unseen environments before actual deployment.
• To reduce reliance on costly and time-consuming post-hoc evaluations by providing predictive benchmarking capabilities.
• To support informed design decisions by identifying environments where a SLAM algorithm is expected to perform well or poorly.
• To improve generalization of SLAM results across different robotic platforms and environments through interpretable, data-driven models.
• To establish a framework for predictive benchmarking applicable to sensor selection, robot configuration, and algorithm choice prior to prototyping.

Proposed method

• The method collects performance data (e.g., localization error) from SLAM algorithms (GMapping, Karto) in a diverse set of simulated 2D environments.
• It extracts geometrical features of each environment using the Voronoi graph, representing the environment's skeleton for analysis.
• A linear regression model is trained to map these Voronoi-based features to the observed SLAM performance metrics (translational and rotational error).
• The trained model predicts performance in new, unseen environments using only their geometric features, without running the SLAM algorithm.
• The approach is validated using real robot data from the AIRLab and simulated runs with the Robocom platform.
• The method supports automated batch training via simulation, enabling scalable and supervised-free dataset generation.

Experimental results

Research questions

• RQ1 Can SLAM algorithm performance in unseen environments be predicted before actual deployment?
• RQ2 How accurately can linear models trained on simulated environments predict real-world SLAM performance using only geometric features?
• RQ3 To what extent does the method generalize across different robotic platforms with varying sensor and kinematic characteristics?
• RQ4 Can predictive benchmarking reduce the need for trial-and-error deployment and support early design-phase decisions?
• RQ5 How interpretable and reliable are the predictions when applied to real-world robot data?

Key findings

• The method predicted translational localization error in real-world experiments with a mean error of 0.111 m, compared to the actual robot result of 0.086 m, showing strong predictive accuracy.
• The predicted rotational error (0.018 rad) was slightly less accurate than the actual value (0.066 rad), likely due to limitations in the robotic platform's odometry.
• The standard deviation of predicted errors was within acceptable ranges, indicating consistent model performance across multiple runs.
• The method achieved a difference of only 2.5 cm in mean translational error between simulation and real robot data, demonstrating strong generalization.
• The model maintained high predictive power even when applied to a different robot platform (Robocom) than used in training, indicating cross-platform robustness.
• The use of linear models ensures interpretability, enhancing trust and applicability in real-world robotic system design.