Ground Segmentation method using Surface Normals Image calculated from Depth Image.

This repository uses depth image to find surface_normal image and ground/non-ground segmented image. The methods used to calculate the surface normals image are given below:

Let $D$ be a depth image of size $(w\times h)$ whose $(i*w + j)$th pixel can be represented as $D_{i,j}$. We assume that the $D$ forms a continuous surface in the real world. For any convex polygon (such as a triangle), a surface normal can be calculated as the vector cross product of two (non-parallel) edges of the polygon. Let's assume that $D_{i,j}$, $D_{i-1,j}$ and $D_{i,j-1}$ lies on the same plane and forms a triangular surface $s$. The normal vector at any point on $s$ is given by

$$\overrightarrow{n}_{i,j} = (D_{i,j} - D_{i-1,j}) \times (D_{i,j} - D_{i,j-1})$$

We first calculate the unit vector of the normal to the surface as

$$\widehat{n}_{i,j} = \overrightarrow{n}_{i,j} / || \overrightarrow{n}_{i,j} ||$$

We get the surface normal image $N$ by applying above equations on $D$. For a ground robot, we define $I_t^b \subseteq { 0, 1 }^{w \times h}$, where 0 represents navigable space (ground) and 1 or 255 is the non-navigable space. The navigability information from $N$ can be obtained as

$$I_t^b = \begin{cases} 1,& \text{if } x=0, y=0, z=1 ~\forall~ \widehat{n}_{i,j} \in N \\\ 0, & \text{otherwise} \end{cases}$$

where $x, y, z$ are the X, Y, and Z components of the unit vector $\widehat{n}_{i,j}$ respectively. We assume that all the surfaces whose normals are pointing upward (i.e. ground) are navigable for ground robots. Typically, we get some non-navigable segments (non-ground) inside the navigable segments and vice-versa in $I^b$. We assume all the navigable surface that is surrounded by non-navigable segments are also non-navigable as they are out of reach. We also assume that anything behind the non-navigable segments is occluded and hence non-navigable.

Prerequisites

• Depth image topic from any simulated or real camera.
• OpenCV
• ROS

Setup

How to Build

Clone and build the repository to your workspace.

mkdir catkin_ws/src -p
cd catkin_ws/src

git clone https://github.com/Dpushp/depth2surface_normals_seg.git

cd ..
catkin_make

Lanuch a robot in simulator with depth camera

Run any robot simulator and verify that you can subscribe to the depth image topic.

Lanuch the depth2surface_normals_seg node

roslaunch depth2surface_normals_seg depth2surface_normals_seg.launch 

Open rviz to visualise the topics

rviz

Parameters Tuning

Run Dynamic Recongigure to change the parameters.

rosrun rqt_reconfigure rqt_reconfigure

Parameters Decription for depth2surface_normals_seg

Parameter	Description	Default Value
~d_erosion_size	The erosion operation is: dst(x,y)=min(x′,y′):element(x′,y′)≠0src(x+x′,y+y′). Used to denoize the depth image	2
~d_gaussian_blur_size	Gaussian blur kernel size used to denoize the depth image.	11
~iLowH	Hue lower limit in surface normals image to select the navigable class. Used to generate the binary navigable image i.e., ground and non-ground segments.	30
~iLowS	Saturation lower limit in surface normals image to select the navigable class. Used to generate the binary navigable image i.e., ground and non-ground segments.	150
~iLowV	Value lower limit in surface normals image to select the navigable class. Used to generate the binary navigable image i.e., ground and non-ground segments.	60
~iHighH	Hue upper limit.	110
~iHighS	Saturation upper limit.	255
~iHighV	Value upper limit.	255
~seg_erosion_size	Erosion kernel size for ground segmentation.	5