论文/arXiv 2609.19533·1 个月前

SLAMSqueezeBench：资源受限条件下的 SLAM 系统比较

针对SLAM基准缺少现实资源约束的问题，提出SLAMSqueezeBench，并在边缘硬件上比较九种SLAM系统。

AI 整理

• 硬件零部件
• 感知与导航
• 端侧部署

原文链接：SLAMSqueezeBench: Comparing SLAM Systems under Resource Constraints（arXiv 2609.19533 · 发布日期：2026-09-17）

SLAMSqueezeBench: Comparing SLAM Systems under Resource Constraints

针对SLAM基准缺少现实资源约束的问题，提出SLAMSqueezeBench，并在边缘硬件上比较九种SLAM系统。

核心要点

• SLAM常与规划、操作等任务共同运行在边缘硬件上，面临严格资源约束。
• 现有SLAM系统多被孤立构建和测试，缺少在现实资源约束下的统一比较机制。
• SLAMSqueezeBench通过限制计算和内存资源，并模拟有限缓冲导致的相机丢帧进行测试。
• 作者用该框架比较九种SLAM系统，覆盖经典方法、学习方法和Gaussian splatting方法，并计划发布。

验证范围与局限：验证范围为边缘硬件上的现实工作负载，比较九种SLAM系统。

英文摘要 (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.

作者：Mohamed Muslim Hefny, Karthik Dantu, Steven Y. Ko

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.19533：SLAMSqueezeBench: Comparing SLAM Systems under Resource Constraints访问 2026-10-21