用多光谱图像提升机器人地形识别，抗光照干扰更强。
Differential Analysis of Multispectral Images for Terrain Identification
- 双流结构融合原始波段与比值特征，增强鲁棒性
- 在油污土壤和草地实验中显著优于基线模型
- 轻量设计适合边缘设备部署，适合野外导航场景
可靠的地形理解是自主机器人导航的前提。然而，广泛使用的基于RGB的感知在低光照、阴影和材料模糊情况下会失效。本文提出DRIFT，一种轻量级多光谱框架，通过双流残差结构和差分融合分支，结合原始光谱波段与光照无关的波段比值表示。波段比值可抑制乘性采集效应（如光照/传感器增益），而差分融合则显式突出绝对波段与比值推导线索之间的差异，从而提升对噪声或部分不可靠光谱测量的鲁棒性。本文（i）在使用无人机搭载MicaSense RedEdge-P相机采集的新油污土壤多光谱数据集上评估DRIFT；（ii）额外开展受控实验，在不同光照及温差（热/冷）条件下分析近红外敏感性。DRIFT在各项测试中均持续优于强基线模型，且保持边缘部署兼容性。
原文摘要 · Abstract (English)
Reliable terrain understanding is a prerequisite for autonomous robot navigation. Yet, the widespread RGB-based perception can fail under low illumination, shadows, and material ambiguities. In this work we propose DRIFT, a lightweight multispectral framework that combines raw spectral bands and illumination-tolerant band-ratio representations through a dual-stream residual architecture and a differential fusion branch. Band ratios attenuate multiplicative acquisition effects (illumination/sensor gains), while the differential fusion explicitly highlights discrepancies between absolute-band and ratio-derived cues, which improves the robustness to noisy or partially unreliable spectral measurements. In the paper (i) we evaluate DRIFT on a new oil-on-soil multispectral dataset acquired using a MicaSense RedEdge-P camera mounted on an Unmanned Aerial Vehicle, and (ii) we provide an additional controlled study on water-on-grass under varying illumination and thermal perturbations (hot/cold water) to analyze NIR-sensitive effects. DRIFT consistently improves over strong baselines, while remaining compatible with edge deployment.
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