SLAMSqueezeBench：资源约束下SLAM系统的比较
SLAMSqueezeBench: Comparing SLAM Systems under Resource Constraints
- Simon Fraser University（西蒙弗雷泽大学）
- University at Buffalo（布法罗大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对现有SLAM基准缺乏资源约束比较机制的问题，提出SLAMSqueezeBench框架，通过限制计算与内存资源并模拟帧丢失，在边缘硬件上比较九个SLAM系统，涵盖经典、学习及高斯溅射方法。
AI中文摘要：
同时定位与地图构建（SLAM）是自主机器人上运行的服务之一，通常用于辅助规划、操作等其他任务。所有这些任务都在边缘硬件上运行，并受到严重的资源约束。然而，大多数SLAM系统是孤立构建和测试的，其性能报告仿佛它们是系统上唯一运行的任务。我们观察到，现有基准缺乏在现实资源约束下比较SLAM系统的通用机制。为解决这一局限，我们开发了SLAMSqueezeBench，一个允许在边缘硬件上于现实工作负载下测试SLAM系统的框架。它通过在执行期间对SLAM系统可用的计算和内存资源施加约束来实现这一点。它还模拟了现实的相机帧采集，当有限缓冲区满时会发生帧丢失。使用SLAMSqueezeBench，我们比较了九个SLAM系统，涵盖经典系统、基于学习的系统以及高斯溅射方法。我们的测试框架将在发表后供社区使用。
英文摘要：
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.
