Using SLAM algorithm to map surrounding environment and determine location of NXT Rover Explorer

Initially a robot have no idea where it is, how it is oriented in space and what an environment looks like. This means that a robot will have to explore its initial position, recognize useful landmarks and use them to get certainty about its localization.

Robot has no idea about its location, environment and position of  ultra sonic sensor

After initial localization step it will be possible to command a robot to got to a point in the unexplored space, defined relative of robot "home" location.

Robot got some data from radar that could be used as constraints to estimate possible location. Still, many locations  are possible, so robot isn't localized yet.

While moving to defined point a robot should detect and avoid any obstacle, build environment map and use it for localization purposes.

SLAM demo:


Some SLAM implementations

• Reference implementation of Sebastian Thurn's group's Fast SLAM algorithm in Java
• EducationalRobots project on Google Code (contains Fast SLAM implementation)

Further resources

• ROS (Robot Operating System) project
• OpenSLAM set of SLAM related projects, algorithms and implementations
• MyRobotLab project on Google Code
• Bashir's Robotics Vision project Using a robot equipped with video camera to generate 2D environment map

Rover Explorer Robot (Mindstorms NXT 2.0, LeJOS)

Inspired by design of

• Lego NXT Radar
• Lego NXT Sonar Explorer
• NXT Distance Viewer
• Map making tribot on Lego NXTLog
• Radar project on NXT Programs

and some code pieces from the "legointernational" project on Google Code I decided to build NXT Mindstorms 2.0 and LeJOS based rover explorer robot matching two main design goals:

• maximum sensing capabilities available in standard NXT 2.0 kit: touch, ultrasonic and color sensors
• durability and ruggedness: use tracks instead of wheels
• simplicity: minimize the number of components

Step 1. Building Rover Explorer Robot platform
Step 2. Implement ultrasonic sensor based feature detection
Step 3. Implement Fast SLAM (Simultaneous Localization and Mapping)
Step 4. Implement environment exploration and motion planning
Step 5. Real-time data exchange with PC and graphical dashboard