Table of contents:

Overview

In this course we take a code first approach to learn about the algorithms and techniques required for Simultaneous Localization and Mapping. We use a Legobot equipped with a 2D LiDAR, motors, controller and caterpillar system. The goal is to learn and implement state of the art localization and mapping models to drive around in an arena with cylindrical obstacles.

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Sensors: The robot consists of a 2D LiDAR scanner as the main sensor and wheel encoders to measure position. The wheel encoder counts the ticks per rotation for both the wheels and this helps us compute the distance travelled by the robot in mm.

Unit A: Motion model and LiDAR based landmark detection

1. Data Representation

Each line of the data follows this format:

  1. Encoder: "Prefix (M), Time Step (in milliseconds), Left Motor Ticks, _, _, _, Right Motor Ticks."
  2. Scan: "Prefix (S), Time Step (in milliseconds), No. of scan points (n), depth measurements ([0,n])”

2. Pose estimation using encoder ticks

Reading the encoder values from the some collected data, we observe that the right wheel steadily increases while the left wheel decreases, meaning the robot makes a left turn.

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