Task-driven SLAM Benchmarking For Robot Navigation
🤖 AI Summary
Existing SLAM benchmarks predominantly emphasize absolute accuracy while neglecting repeatability—a critical metric for navigation tasks—leading to evaluations poorly correlated with real-world deployment robustness. To address this, we propose TaskSLAM-Bench: the first SLAM benchmark framework explicitly designed around repeatability and tailored to mobile assistive robotics for elderly care. It integrates a task-driven evaluation paradigm with a reproducible navigation performance measurement protocol, enabling joint mapping and localization assessment in both simulation and real-world settings. TaskSLAM-Bench conducts the first repeatability benchmarking of passive stereo SLAM against LiDAR SLAM in representative indoor environments, demonstrating comparable localization accuracy between the two modalities. All code, evaluation pipelines, and configuration scripts are fully open-sourced, facilitating user-defined environment deployment and validation. This framework bridges the gap between benchmarking metrics and operational reliability in assistive robotic navigation.
Application Category
📝 Abstract
A critical use case of SLAM for mobile assistive robots is to support localization during a navigation-based task. Current SLAM benchmarks overlook the significance of repeatability (precision), despite its importance in real-world deployments. To address this gap, we propose a task-driven approach to SLAM benchmarking, TaskSLAM-Bench. It employs precision as a key metric, accounts for SLAM's mapping capabilities, and has easy-to-meet implementation requirements. Simulated and real-world testing scenarios of SLAM methods provide insights into the navigation performance properties of modern visual and LiDAR SLAM solutions. The outcomes show that passive stereo SLAM operates at a level of precision comparable to LiDAR SLAM in typical indoor environments. TaskSLAM-Bench complements existing benchmarks and offers richer assessment of SLAM performance in navigation-focused scenarios. Publicly available code permits in-situ SLAM testing in custom environments with properly equipped robots.
Problem
Research questions and friction points this paper is trying to address.
Addresses lack of repeatability in SLAM benchmarks
Proposes TaskSLAM-Bench for navigation-focused SLAM evaluation
Compares precision of visual and LiDAR SLAM in indoor environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Task-driven SLAM benchmarking approach
Precision as key metric in SLAM
Simulated and real-world SLAM testing
🔎 Similar Papers
No similar papers found.
