用深度相机图像训练端到端模型，让机器人自动避障
Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
- 融合彩色与深度图像，直接输出转向速度指令
- 在不同光照和动态障碍环境下测试，误差低至0.034
- 适合想快速部署视觉避障系统的机器人开发者
移动机器人在已知与未知环境中的导航中，障碍物避让至关重要。本研究设计、训练并测试了两个定制的卷积神经网络（CNN），以深度相机获取的彩色和深度图像为输入，通过传感器融合生成机器人的角速度输出，作为转向指令。在多种光照条件和动态障碍物环境下采集了新的视觉导航数据集。数据采集过程中，通过Wi-Fi建立远程服务器与机器人之间的通信链路，使用机器人操作系统（ROS）话题传输速度指令，实现视觉数据与对应转向指令的同步记录。采用均方误差、方差分数和前向传播时间等评估指标，对两个网络进行了清晰比较，明确了适用于实际应用的模型。
原文摘要 · Abstract (English)
Obstacle avoidance is crucial for mobile robots' navigation in both known and unknown environments. This research designs, trains, and tests two custom Convolutional Neural Networks (CNNs), using color and depth images from a depth camera as inputs. Both networks adopt sensor fusion to produce an output: the mobile robot's angular velocity, which serves as the robot's steering command. A newly obtained visual dataset for navigation was collected in diverse environments with varying lighting conditions and dynamic obstacles. During data collection, a communication link was established over Wi-Fi between a remote server and the robot, using Robot Operating System (ROS) topics. Velocity commands were transmitted from the server to the robot, enabling synchronized recording of visual data and the corresponding steering commands. Various evaluation metrics, such as Mean Squared Error, Variance Score, and Feed-Forward time, provided a clear comparison between the two networks and clarified which one to use for the application.
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