Low-power parallel algorithms for single image based obstacle avoidance in aerial robots
Low-power parallel algorithms for single image based obstacle avoidance in aerial robots
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DOI：
10.1109/iros.2012.6386146
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
Ian Lenz;Mevlana Gemici;Ashutosh Saxena
中科院分区：
文献类型：
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作者：
Ian Lenz;Mevlana Gemici;Ashutosh Saxena
对于空中机器人来说，感知和躲避障碍物是在混乱的未知环境中自主运行的必要技能。在这项工作中，我们使用从机载摄像机捕获的单个图像作为输入，生成障碍物分类，并使用它们来选择规避机动。我们提出了一种基于马尔可夫随机场的方法，该方法将障碍物建模为图像邻近区域的视觉特征和非局部依赖关系的函数。我们使用新的低功耗并行神经形态硬件执行高效推理，其中信念传播更新使用泄漏集成和并行火神经元完成，同时消耗不到1 W的功率。在室外机器人实验中，我们的算法能够始终如一地生成干净、准确的障碍物地图，使我们的机器人能够避开各种各样的障碍物，包括树木、电线杆和围栏。
For an aerial robot, perceiving and avoiding obstacles are necessary skills to function autonomously in a cluttered unknown environment. In this work, we use a single image captured from the onboard camera as input, produce obstacle classifications, and use them to select an evasive maneuver. We present a Markov Random Field based approach that models the obstacles as a function of visual features and non-local dependencies in neighboring regions of the image. We perform efficient inference using new low-power parallel neuromorphic hardware, where belief propagation updates are done using leaky integrate and fire neurons in parallel, while consuming less than 1 W of power. In outdoor robotic experiments, our algorithm was able to consistently produce clean, accurate obstacle maps which allowed our robot to avoid a wide variety of obstacles, including trees, poles and fences.