SLAMSqueezeBench: Comparing SLAM Systems under Resource Constraints

2026-09-16

AI Summary

为解决SLAM系统在资源受限条件下的比较问题，开发了SLAMSqueezeBench框架，通过模拟真实工作负载和施加计算、内存限制来测试不同SLAM系统的性能。

Abstract

Simultaneous localization and mapping (SLAM) is one of the services running on an autonomous robot. It is typically run to assist other tasks such as planning, manipulation, etc. All these tasks are run on edge hardware and are subject to severe resource constraints. However, most SLAM systems are built and tested in isolation, and their performance is reported as if they are the only task running on a system. We observe that existing benchmarks lack a common mechanism for comparing SLAM systems under realistic resource constraints. To address this limitation, we have developed SLAMSqueezeBench, a framework that allows testing of SLAM systems under realistic workloads on edge hardware. It does so by imposing constraints on compute and memory resources available for the SLAM system during execution. It also simulates realistic camera frame acquisition with frame drops when a finite buffer is full. Using SLAMSqueezeBench, we compare nine SLAM systems spanning classical systems, learning-based systems, and approaches for Gaussian splatting. Our testing framework will be available for use by the community upon publication.

Problem

• SLAM
• resource constraints
• edge hardware
• performance comparison
• realistic workloads

Innovation

• SLAM
• Resource Constraints
• Edge Hardware
• Benchmarking