The present research on the multi-sensor information fusion technology of mobile robots aims to better understand the outdoor scene and improve the robot's perception of the environment. Firstly, a conversion algorithm is proposed based on two point-cloud-to-image algorithms, including the point cloud plane fitting and point cloud projection transformation. Moreover, the elevation map is constructed to describe the terrain characteristics of the scene based on the three-dimensional laser ranging data. Meanwhile, the conditional random field model is used to obtain landform characteristics from visual information. Besides, the projection transformation and information statistics methods are used to effectively integrate the laser information and the visual information with the grid in the elevation map as the carrier. Ultimately, the convolution neural network is used to realize the three-dimensional scene understanding. It is found that the average recognition rate of the outdoor scene understanding model based on multi-sensor information fusion is as high as 89.36%, and the image segmentation time of the proposed algorithm is not more than 180 ms.The latest research results refer to the use of SSAE in combination with the CRF algorithm. On the whole, the proposed model improves the real-time performance of the mobile robot under the premise of accuracy, and realizes the recognition and analysis ability of complex scenes through the construction of multi-sensor information. This study has important practical significance for promoting the development of the mobile robot autonomous industry.

目前对移动机器人多传感器信息融合技术的研究旨在更好地了解室外场景，提高机器人对环境的感知能力。首先，提出了一种基于点云平面拟合和点云投影变换两种点云转图像算法的转换算法。此外，基于三维激光测距数据构建高程图来描述场景的地形特征。同时，利用条件随机场模型从视觉信息中获取地形特征。此外，利用投影变换和信息统计方法，以高程图中的网格为载体，有效整合激光信息和视觉信息。最终，神经网络用于实现三维场景理解。发现基于多传感器信息融合的户外场景理解模型平均识别率高达89.36%，所提算法的图像分割时间不超过180 ms。最新研究成果参考SSAE 与 CRF 算法的结合使用。总体而言，该模型在准确率的前提下提高了移动机器人的实时性，通过多传感器信息的构建，实现了对复杂场景的识别和分析能力。本研究对于推动移动机器人自主产业的发展具有重要的现实意义。