Landmark-Detection-Tracking-SLAM

This project implements SLAM (Simultaneous Localization and Mapping) for a 2 dimensional world which is used for robot sensor measurements and movement to create a map of an environment from only sensor and motion data gathered by a robot, over time. SLAM gives a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is a basic concept implementation and this topic is an active area of research in the fields of robotics and autonomous systems.(refer to reference)

Implementation Approach

Code Structure

The project will be broken up into three Python notebooks; the first two are for exploration of provided code, and a review of SLAM architectures:

• Step 1 : Robot Moving and Sensing: Testing with imported code from robot_class.py
• Step 2 : Omega and Xi, Constraints
• Step 3 : Landmark Detection and Tracking with robot_class.py

robot_class.py

####sense function

• compute dx and dy, the distances between the robot and the landmark
  • The noise component should be a random value using self.rand() between (-1.0, 1.0)*measurement_noise
• account for measurement noise by adding a noise component to dx and dy
• If either of the distances, dx or dy, fall outside of the internal var, measurement_range, don't add to list, otherwise, add them to the measurements list
• returns a measurement list of values that reflect the measured distance (dx, dy) between the robot's position and any landmarks it sees with a given amount of measurement_noise and the measurement_range of the robot. One format in the returned list looks like this: [landmark_index, dx, dy].

Landmark Detection and Tracking

initialize_constraints

Initialize the array omega and vector xi such that any unknown values are 0 the size of these should vary with the given world_size, num_landmarks, and time step, N, parameters.

SLAM(Simultaneous Localization and Mapping)

• Iterate through the generated data and update the constraints in sensor measurement data.
  • The values in the constraint matrices is updated by sensor measurements from measurement_uncertainty
• Update the constraint matrices from robot motion data.
  • The values in the constraint matrices is updated by motion (dx, dy) and uncertainty in motion.
  • It shows the robot is moving as per the motion
• slam returns a list of robot and landmark positions, mu.
  • mu is the x, y positions of the robot over time and the estimated locations of landmarks in the world.
  • mu is calculated with the constraint matrices omega^(-1)*xi

Reference

• SLAM
• Pose Estimation with Multiple Sensors
• General SLAM Framework
• robotic SLAM