dhyeyr-007 / robot-mapping

Developed and implemented all the occupancy mapping based sensor models for mobile robot mapping and successfully implemented the BKI Semantic mapping pipeline.

Robot-Mapping

Detailed development and implementation of:

• Discrete Counting Sensor Model (D-CSM);
• Continuous Counting Sensor Model (C-CSM);
• Discrete Semantic Counting Sensor Model (D-SCSM) &
• Countinuous Semantic Counting Sensor Model (C-SCSM)
• BKI Semantic Mapping

Sensor Model / Occupancy Mapping

Model & Info	Map	Variance Map
D-CSM
Implemented a 2D counting sensor model (CSM) for occupancy grid mapping in ogm_CSM.py. Visualization of the map is with grid_size = 0.135 m and its associated variance map
C-CSM
Implemented a 2D continuous counting sensor model (CSM) in ogm_continuous_CSM.py. Visualization of the map is with grid_size = 0.135 m and its associated variance map
....... with grid_size = 0.270 m
....... with grid_size = 0.500 m
D-SCSM
Implemented a semantic counting sensor model (S-CSM) in ogm_S_CSM.py. Visualization of the map is with grid_size = 0.135 m and variance map of the class with highest probabilities at each grid
C-SCSM
Implemented a continuous semantic counting sensor model (S-CSM) in ogm_continuous_S_CSM.py. Visualization of the map is with grid_size = 0.135 m and variance map of the class with highest probabilities at each grid

BKI Semantic Mapping Implementation

Build and run the BKI Semantic Mapping on KITTI semantics dataset sequence 04.
Notice the system dependencies:

• Ubuntu system. The mapping algorithm is build on Ubuntu system. It has been tested on Ubuntu 16.04 and Ubuntu 18.04. If you didn’t have an Ubuntu system, you could create a virtual machine.
• ROS system. The mapping algorithm has been tested on ROS kinetic and ROS melodic. You can follow the installation guide for ROS in the documentation.
• A catkin workspace (catkin_ws). You can follow the steps in the tutorial. Read the README in the BKI semantic repository and do the following steps:

1. Build the repository with catkin. Run the following commands in your catkin workspace (catkin_ws): • cd src/ • git clone https://github.com/ganlumomo/BKISemanticMapping • cd .. • catkin_make • source catkin_ws/devel/setup.bash
2. Download semantic KITTI dataset sequence 04 data from https://drive.google.com/file/d/19Dv1jQqf-VGKS2qvbygFlUzQoSvu17E5/view and uncompress it into the data folder.
3. Run the demo with the following command: • roslaunch semantic_bki semantickitti_node.launch

Simulation