面向紧耦合运动辅助检测的杂乱环境鲁棒遮挡避免
Clutter Resilient Occlusion Avoidance for Tightly-Coupled Motion-Assisted Detection
AI总结：
本文提出运动辅助检测框架CROA，通过凸-凹过程和双层优化在避碰约束下最小化遮挡概率，在多车道城市场景中显著提升了点密度、遮挡比例和检测误差指标。
摘要：
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，主动规划可执行路径，使机器人能在新视点观测目标以潜在降低遮挡。与在杂乱环境中可能失效的现有MAD方法不同，所提框架在此类场景中具有鲁棒性，故称为杂乱环境鲁棒遮挡避免（CROA）。CROA的关键在于通过凸-凹过程和基于对偶的双层优化，在基于多面体的避碰约束下最小化遮挡概率。系统实现支持基于lidar的MAD，交织执行基于学习的检测和基于优化的规划。实验表明，在多车道城市场景中，CROA在稀疏卷积神经网络检测器下，于点密度、遮挡比例和检测误差方面优于多种MAD方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
