Wali turtlebot is a self-driving turtlebot, which uses Scene Segmentation on RGBD data to make Path Planning for turtlebot. This process is shown in the following picture.
The hardware device we use:
- Turtlebot2
- HIKVISION wireless camera (stereo)
- Microsoft Kinect v1
- HiSilicon970 (arm)
- others, little computer host, Intel RealSense R200
We use a kinect v1 to obtain RGB and Depth data now, in the future, we'll replace it with the stereo camera composed of two HIKVISION wireless cameras.
There are 2 reasons why we use stereo rather than kinect.
- kinect can't obtain intense depth image.
- kienct can't work well in outdoor environment.
The following picture shows the evolution process of Wali turtlebot.
The core technology we use:
- Bilateral Semantic Segmentation on RGBD data (BiSeNet-RGBD, 20fps on Nvidia Quadro P5000)
- ROS robot nodes communication mechanism
- Turtlebot motion control using rospy (forward, left, right, back, and smoothly speed up)
- Depth-based direction choose if not use the neural network（choose the direction with the largest depth）
BiSeNet-RGBD architecture is shown below.
BiSeNet-RGBD is trained on Princeton SUN-RGBD dataset. Now it can predict 37 class, we'll annotate some specific classes in our practical scenario using labelme in the future.
Test scenes：There are 10 scenes including 4 indoors and 6 outdoors, which are used to test model performance. The test results are list in part 3.
By using this architecture, we've made some drive test in the real scenario.
The test videos are shown in part 3.
- 4 indoor scenes
- 6 outdoor scenes
- stereo vision
We also test out model on RGBD data obtained by stereo camera.
The test results is shown in the video: https://www.bilibili.com/video/av44357263/
未设置匀变速运动，速度突变卡顿
添加匀变速运动之后，卡顿问题解决
- in1：https://www.bilibili.com/video/av44315156/
- in2：https://www.bilibili.com/video/av44315266/
- out1：https://www.bilibili.com/video/av44315515/
- out2：https://www.bilibili.com/video/av44315582/
updating...