Start Date
1-5-2026 12:00 PM
End Date
1-5-2026 1:00 PM
Description
This research presents a comparative analysis of biologically-inspired SLAM models (RatSLAM [2], SeqSLAM [3]) versus traditional feature-based geometric SLAM (ORB-SLAM3 [1]) to establish a standardized framework for evaluating accuracy, robustness, and computational efficiency under varying environmental conditions. To ensure an equitable baseline, models are subjected to a unified testing pipeline across real-world datasets (Oxford RobotCar clear and overcast sequences) and controlled Webots simulations (day/night and seasonal centerlines) using timestamp-aligned 480p monocular PNG images. Within this pipeline, pose estimations are standardized to the TUM trajectory format, and spatial alignment and scale correction are applied via the evo evaluation library [4] to extract Absolute Pose Error (APE) and Relative Pose Error (RPE) metrics. By establishing this model-agnostic evaluation framework, this methodology provides a standardized baseline for the direct, objective comparison of SLAM performance. Ongoing research will leverage this established pipeline to benchmark algorithmic energy efficiency (Joules per frame) via SLAMBench and assess real-world battery variance during physical mobile robot deployments.
An evaluation of biologically-inspired SLAM models against traditional models
This research presents a comparative analysis of biologically-inspired SLAM models (RatSLAM [2], SeqSLAM [3]) versus traditional feature-based geometric SLAM (ORB-SLAM3 [1]) to establish a standardized framework for evaluating accuracy, robustness, and computational efficiency under varying environmental conditions. To ensure an equitable baseline, models are subjected to a unified testing pipeline across real-world datasets (Oxford RobotCar clear and overcast sequences) and controlled Webots simulations (day/night and seasonal centerlines) using timestamp-aligned 480p monocular PNG images. Within this pipeline, pose estimations are standardized to the TUM trajectory format, and spatial alignment and scale correction are applied via the evo evaluation library [4] to extract Absolute Pose Error (APE) and Relative Pose Error (RPE) metrics. By establishing this model-agnostic evaluation framework, this methodology provides a standardized baseline for the direct, objective comparison of SLAM performance. Ongoing research will leverage this established pipeline to benchmark algorithmic energy efficiency (Joules per frame) via SLAMBench and assess real-world battery variance during physical mobile robot deployments.