用轻量Mamba模型实现边缘设备高效目标检测，支持智能导航
EdgeNavMamba: Mamba Optimized Object Detection for Energy Efficient Edge Devices
- 基于强化学习框架，用Mamba模型做高效目标检测
- 模型缩小67%，单次推理能耗降73%，性能不变
- 适合资源受限的机器人导航场景
在计算资源受限的边缘设备上部署高效且准确的深度学习模型，一直是自主导航领域的挑战，尤其在实时应用中。本文提出EdgeNavMamba，一种基于强化学习的目标导向导航框架，采用高效的Mamba对象检测模型。为训练和评估检测器，我们构建了一个在多样化室内环境中采集的自定义形状检测数据集，反映真实导航中的视觉线索。该检测器作为预处理模块，从视觉输入中提取边界框（BBOX），再传递给强化学习策略以控制导航。实验结果表明，在NVIDIA Jetson Orin Nano和Raspberry Pi 5等边缘设备上，学生模型大小减少67%，单次推理能耗降低最高达73%，同时保持与教师模型相同的性能。在MiniWorld和IsaacLab仿真器中，相比基线模型参数减少31%，仍维持高检测精度。在MiniWorld仿真器中，导航策略在不同复杂度环境中均达到超过90%的成功率。
原文摘要 · Abstract (English)
Deployment of efficient and accurate Deep Learning models has long been a challenge in autonomous navigation, particularly for real-time applications on resource-constrained edge devices. Edge devices are limited in computing power and memory, making model efficiency and compression essential. In this work, we propose EdgeNavMamba, a reinforcement learning-based framework for goal-directed navigation using an efficient Mamba object detection model. To train and evaluate the detector, we introduce a custom shape detection dataset collected in diverse indoor settings, reflecting visual cues common in real-world navigation. The object detector serves as a pre-processing module, extracting bounding boxes (BBOX) from visual input, which are then passed to an RL policy to control goal-oriented navigation. Experimental results show that the student model achieved a reduction of 67% in size, and up to 73% in energy per inference on edge devices of NVIDIA Jetson Orin Nano and Raspberry Pi 5, while keeping the same performance as the teacher model. EdgeNavMamba also maintains high detection accuracy in MiniWorld and IsaacLab simulators while reducing parameters by 31% compared to the baseline. In the MiniWorld simulator, the navigation policy achieves over 90% success across environments of varying complexity.
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