发表机构
Electronics and Telecommunications Research Institute (ETRI); Pusan National University; Jeonbuk National University（韩国电子通信研究院; 釜山国立大学; 全北国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过GPU-NPU平台实验证明孤立评估会错误排序导盲机器人多摄像头感知的加速器放置，提出应报告争用扫描、截止时间错失率和最差流sAP以指导部署。
AI 中文摘要
多摄像头流式感知日益部署在与共存工作负载共享的异构边缘平台上，然而加速器放置通常使用孤立的单流实验和平均流式平均精度（sAP）进行评估。在单个GPU-NPU平台上使用两条端到端流水线，我们表明孤立评估可能错误排序部署时的放置方案。尽管GPU流水线在孤立情况下更受青睐，但GPU本地化争用引入的截止时间错失使检测变得过时，并可能在GPU完全饱和之前逆转偏好的放置方案。NPU流水线在孤立情况下对小物体和中物体的精度低于GPU流水线，但在大物体上几乎与之匹配。在我们的延迟和争用实验中，最大的绝对sAP损失发生在大物体上。在我们的四流实验中，偏好的放置方案取决于哪条路径变得过时，增加GPU侧争用将最佳放置从全GPU转移到全NPU。在GPU饱和的视觉-语言共存负载下，全NPU的最差流sAP是全GPU的5.2倍。由于平均sAP可能隐藏严重的单流退化，评估应报告争用扫描、两条路径上的截止时间错失率以及最差流sAP与平均sAP。
英文摘要
Robotic guide dogs should understand their surroundings, objects, and potential risks. Prior research has focused on raw sensor data from cameras and 2D or 3D LiDAR, which precisely measure distance points rather than provide a semantic understanding of the scene. While these physical measurements are effective for robot-centric collision avoidance and robot safety, they are not suitable for human-centric guidance. The system should recognize the type and relevance of obstacles and explain them, clearly and actionably, in terms of their spatial relation to the user. We present complete on-device perception modules that fuse a 360 camera and a 2D LiDAR for reliable collision avoidance, with moving-object detection and tracking for human-centric guidance. Finally, in walking-impossible situations, a vision--language model delivers pathway explanations as a safety mechanism to reduce user anxiety. In experiments, verification of fused 360 camera--LiDAR depth shows reliable near-range perception but inherent mid-range bias, while the system as a whole sustained real-time performance under 55 W. On the real-world egocentric GuideDogQA benchmark, our system achieved 83.8\% accuracy, compared with 67.1\% for GPT-4o. These results demonstrate that practical human-centric guidance with real-time on-device inference is feasible even on quadrupeds.
Commentsaccepted in ACCV 2026
