实时的移动机器人语义地图构建系统

语义信息可以使机器人更充分地理解未知环境,为更高级的人机交互和完成更复杂的任务奠定基础。为了能够使移动机器人实时地创建语义地图,在Jetson TX1嵌入式电脑上开发了一种轻量级的深度学习目标检测模型,在保证了检测精度的同时,实现了高效的目标检测功能。并利用了视频流中的帧间光流信息,使用运动信息指导传播算法降低检测算法的漏检率。对于Kinect传感器生成的深度图像有黑边、黑洞等缺陷,使用统一计算设备架构(CUDA)技术开发了一种实时的深度图像修复算法。利用即时定位与地图构建(SLAM)技术,实现移动机器人底层的定位、导航、地图创建功能,并在此基础上使用贝叶斯推理框架,同时融合了环境的度量信息与视觉识别信息完成了语义地图的创建。经过实验表明,所提出的方法在实际的、复杂的室内环境下可以使移动机器人实时地创建语义地图。

System of real time mobile robot semantic map building

Semantic information can help the robot to better understand unknown environment and lay the foundation for more advanced human computer interaction and more complicated task. To enable mobile robot to build semantic map in real time, a light deep learning model is developed for object detection on embedded computer Jetson TX1. The inter frame optical flow information in the video stream is used to reduce the missing rate of object detection algorithm, which is called motion guided propagation (MGP) algorithm. A real time depth map restoration algorithm based on CUDA is utilized because the depth map generated by Kinect has black hole and black border. SLAM technology is employed in this paper for robot location, navigation and mapping. On this basis, Bayesian inference framework is integrated with measurement information of environment and object detection information to complete the building of semantic map. Experiments show that the proposed method can enable the mobile robot to build the semantic map in real time in the real, complicated indoor environment.