让机器人在杂乱环境里自动找好视角，避免被遮挡
Clutter Resilient Occlusion Avoidance for Tightly-Coupled Motion-Assisted Detection
- 通过路径规划主动换视角，降低遮挡概率
- 在多车道城市场景中，遮挡率和检测误差显著更低
- 适合复杂动态环境下的机器人视觉感知应用
遮挡是导致检测失败的关键因素。本文提出一种运动辅助检测（MAD）方法，通过主动规划可执行路径，使机器人在新视角下观测目标，从而可能减少遮挡。与现有MAD方法在杂乱环境中易失效不同，所提框架具备鲁棒性，因此称为抗杂乱遮挡（CROA）。CROA的核心在于在基于多面体的碰撞规避约束下，利用凸-凹过程与基于对偶的双层优化最小化遮挡概率。系统实现支持基于激光雷达的MAD，融合学习型检测与优化型规划的交替执行。实验表明，在稀疏卷积神经网络检测器下，CROA在多车道城市驾驶场景中，相较于多种MAD方案，在点密度、遮挡率和检测误差方面均有提升。
原文摘要 · Abstract (English)
Occlusion is a key factor leading to detection failures. This paper proposes a motion-assisted detection (MAD) method that actively plans an executable path, for the robot to observe the target at a new viewpoint with potentially reduced occlusion. In contrast to existing MAD approaches that may fail in cluttered environments, the proposed framework is robust in such scenarios, therefore termed clutter resilient occlusion avoidance (CROA). The crux to CROA is to minimize the occlusion probability under polyhedron-based collision avoidance constraints via the convex-concave procedure and duality-based bilevel optimization. The system implementation supports lidar-based MAD with intertwined execution of learning-based detection and optimization-based planning. Experiments show that CROA outperforms various MAD schemes under a sparse convolutional neural network detector, in terms of point density, occlusion ratio, and detection error, in a multi-lane urban driving scenario.
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