论文标题
VO：使用商品传感器的行人之间有效导航
OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors
论文作者
论文摘要
我们提出了一种修改后的速度 - 启动（VO）算法，该算法使用对环境的概率部分观察来计算速度并将机器人导航到目标。我们的系统使用商品视觉传感器，包括单相机和2D激光雷达，通过光流估计，对象检测和传感器融合来明确预测周围障碍物的速度和位置。我们工作的一个关键方面是耦合（：光流）和可靠导航的计划（VO）组件（VO）组件。总体而言，在导航时间和避免碰撞的成功率方面，我们使用基于学习的感知和基于模型的计划方法的VO算法提供了比以前的算法更好的性能。我们的方法还提供了概率碰撞回避算法的界限。我们强调了VO的实时性能在模拟和现实世界中的行人中导航的海龟机器人。可以通过https://gamma.umd.edu/ofvo/获得演示视频
We present a modified velocity-obstacle (VO) algorithm that uses probabilistic partial observations of the environment to compute velocities and navigate a robot to a target. Our system uses commodity visual sensors, including a mono-camera and a 2D Lidar, to explicitly predict the velocities and positions of surrounding obstacles through optical flow estimation, object detection, and sensor fusion. A key aspect of our work is coupling the perception (OF: optical flow) and planning (VO) components for reliable navigation. Overall, our OF-VO algorithm using learning-based perception and model-based planning methods offers better performance than prior algorithms in terms of navigation time and success rate of collision avoidance. Our method also provides bounds on the probabilistic collision avoidance algorithm. We highlight the realtime performance of OF-VO on a Turtlebot navigating among pedestrians in both simulated and real-world scenes. A demo video is available at https://gamma.umd.edu/ofvo/
