Localization and 3D Mapping for Indoor Environment

Simultaneous Localization and Mapping (SLAM) is a precondition for some robot applications, such as industrial automation, autonomous vehicles, and collision-less navigation.The role of SLAM has been increased with the advancement in the sensor technologies and the availability of low cost and effec

Project Title

Localization and 3D Mapping for Indoor Environment

Project Area of Specialization

Robotics

Project Summary

1) Main Reasons:

Simultaneous Localization and Mapping (SLAM) is a precondition for some robot applications, such as industrial automation, autonomous vehicles, and collision-less navigation.The role of SLAM has been increased with the advancement in the sensor technologies and the availability of low cost and effective SLAM. The main objective of project is to design a “SLAM BASED 3D MAP BUILDING USING 2 LOW COST 2D LASER SCANNERS (RPLIDAR A1)”and perform motion planning in real-time from the initial position to the final destination (surveyed region).  

The main reason that made SLAM as one of the most prominent field for domestic and industrial applications is its adoptability to various environments by utilizing heterogeneous sensors. The SLAM has become the essential part of many industrial operations, the SLAMS are more popular in multiple tasks such as cleaning, educational and assistive applications The most challenging task for any SLAM during performing assigned job is to localize itself in the region and to perceive the surrounding at the same time.

2) Need of Design:

Our goal is to develop a real-time 3D MAP BASED SLAM that can plan motion from its beginning position to its final destination (surveyed region), move the SLAM with smooth navigation, overcome the obstacles in the region's path, and finally arrive at its target as programmed. Another main goal is to develop a low cost 3d map based SLAM using 2d laser scanner (rplidar A1) at affordable rates.

Project Objectives

1) Design of SLAM’s Vehicle:

 The complete design of the vehicle will fabricate into two sections. The lower section of the vehicle will consist on 4 driving motors (2 encoder motors for front wheel and 2 simple motors for back wheel), 4 wheels, and the upper section of the vehicle will consist on main controller(PC), other sensors at upper section we will use a acyclic platform to mount 1 RP LIDAR in horizontal and other for vertical scanning. We will connect the scanners, sensors and electronic boards wirelessly with ROS running on the laptop. ROS has the ability to compile the data from the multiple sensors and to store them to process for offline work. The laptop is the main controller that will directly used to interface with the two RPLidars A1 scanners. The Arduino UNO controller will use for driving the SLAM’S Vehicle as per received command from the main controller(PC). It controls the wheel through L298N, a dual H bridge driver unit. One more task for this Arduino UNO is to take wheels’ feedback using optical encoders integrated at front wheels.

2) 3D Map Development:

The horizontal scanner is used to estimate 2D map and pose of the vicinity and vertical scanner data has been transformed into referenced frame of horizontal scanner in offline mode using python coding. In last by incorporating all recorded scans at unique time stamps along with respective pose values of the SLAM, the complete 3D point cloud map of the navigated vicinity will produce in offline process. In last we will verify 3d map dimensions by establishing Building Information Model (BIM) on famous software Auto-Desk.

3) scanning and Processing:

After the vertical and horizontal scanning, the scanned data will send to the main controller The complete information will wirelessly recorded at the ROS PC (laptop) for offline processing of the data using python to combine both horizontal and vertical scans, to perform segmentation of 3D point cloud data and to merge individual maps.

4) Use of Robotics Operating System (ROS)

ROS is supposed to be the heart of Robotics learning, development, and implementation. The use of ROS is an objective to make the SLAM easy to use and open for all future working and development worldwide.

Project Implementation Method

1) Pose Estimation:

The first step for SLAM is Pose Estimation, Pose Estimation The estimation of the full 6 degrees of freedom robot pose and twist is realized in the pose estimation node that implements an Extended Kalman Filter (EKF) and fuses measurements from an inertial measurement unit (IMU), the ... PointPoseNet: Point Pose Network for Robust 6D Object Pose Estimation. It contains the Extended Kalman Filter (EKF) that estimates the 6DOF pose of the robot.

2) Location Recognition:

The first step for SLAM is Location Recognition: To understand the environment(location) and to ensure that the map is look like that map (surveyed region) which we will program.

3) Motion Planning:

After recognition of environment(location), SLAM’S vehicle need motion planning to reach out to the destination in real-time from the initial position to the final destination (surveyed region). For motion planning, we will use package inside the Robotic Operating System (ROS) for motion planning algorithms.

Benefits of the Project

Benefits:

The Localization and 3D Mapping for Indoor Environment will be made for creating simplicity in the Era of New Technology. Its key benefit is its adaptability to various Environment through the use of heterogeneous sensors.

Mobile robots has become an integral part of many industrial processes, including some that are potentially harmful to human workers and require high precision and accuracy. Mobile robots are more common in domestic applications for a variety of functions such as cleaning, educational, and assistive applications.

Where as 2D Mapping which is used now a days are very less efficient therefore we are using 3D Mapping for the increasing the efficieny of SLAM. It is also used as a Long Term Project.

 

Technical Details of Final Deliverable

Final Deliverable:

The deliverables of Localization and 3D Mapping for Indoor Environment are as below:

1) 2-RP Lidar.
2) PC (main controller).
3) Encoder Motors.
4) Arduino Nano.

2-RP Lidar used to show Mapping of surveyed region Horizontically and Vertically. PC (main controller) is connected with both Scanners. Encoder Motors are used for moving Slams vehicle thorough H-bridge. Arduino Nano is connected with main controller for signal tracking.

Final Deliverable of the Project

Hardware System

Core Industry

Transportation

Other Industries

Core Technology

Robotics

Other Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com