基于结构光的室内消防机器人环境重构研究

Research on environment reconstruction of indoor fire fighting robot based on structured light

为了提高消防机器人在室内火场中的环境感知与重构能力,提出了一套基于结构光深度相机的室内二维环境重构方法。首先通过融入IMU(Inertial measurement unit,IMU)姿态传感器来对相机获取的深度数据修正补偿;其次在世界坐标系下通过区间阈值高度过滤算法对障碍物和道路进行区分,并生成二维点云图来进行环境重构;最后将相机与机器人进行动态建模分析,修正安装误差并更新全局环境地图。算法在室内自主消防机器人平台上进行导航避障测试,结果表明,提出的方法有效提高了消防机器人在复杂火场环境中的二维环境重构与感知能力,使机器人精准自主避障,快速前往着火点实施灭火。研究对室内消防机器人的导航及避障具有重要意义。

Stereo depth cameras,RGB-D cameras and other environment construction sensors based on color images will produce large errors or even fail to measure in a complex indoor fire environment with a lot of smoke and darkness,leading to a serious decline of autonomous fire-fighting robots in the environmental perception and reconstruction capabilities,then it won't be able to effectively detect and bypass unknown obstacles.Besides,multiline lidar is expensive and has low indoor utilization.In order to improve the environment perception and reconstruction capabilities of autonomous fire-fighting robots in indoor fire scenes,this paper proposes a method for indoor 2D environment reconstruction based on structured light depth camera.Firstly,using the IMU attitude sensor correct the depth data;secondly,use the Area-Threshold-Height-Filtering algorithm to distinguish obstacles and roads in the world coordinate system,then we can get the map of 2D point cloud.Finally,build the dynamic model of the camera and the robot,so that we can fix installation errors and update the global environment map.We tested the improved algo rithm on the indoor autonomous fire-fighting robot platform.The results show that the improved algorithm effectively improves the environment reconstruction and perception capabilities of autonomous fire-fighting robots in the complex fire scene,and the robot can accurately avoid obstacles and quickly go to the fire area to put out the fire.The article has a great significance of autonomous fire-fighting robots about navigation and obstacle avoidance.