Solving the Indoor SLAM Problem for a Low-Cost Robot Using Sensor Data Fusion and Autonomous Feature-Based Exploration

PhD Student: Prof. MSc. Luciano Buonocore (UFMA)Advisor: Prof. Dr. Cairo Lúcio Nascimento Júnior (ITA)

Three software modules run in an integrated form

Environment features measured

3 motions usingOdometric model from goal selected

Estimated map at the moment

- 3 types of sensors: a)Visual (wireless CAM + Laser)b)Infrared (two units) c)Sonar

- Softwares:a)PC: overall system intelligence (SLAM filter, Data fusion and Autonomous Exploration).b) Robot: Mutli-Threading C code that executes basic commands, distance measures and some status.

- Communication PC-robot: IP Wireless.

PROPOSED SENSOR DATA FUSION ALGORITHM Experiment to evaluate the mapping accuracy of the algorithm

PROPOSED AUTONOMOUS FEATURE-BASED EXPLORATION

Basic tasks: a)Goals select →locally (1) or environment opening (2)b)Finish condition (of the exploration task)

SLAM EXPERIMENT IN A SMALL INDOOR ENVIRONMENT

WITHOUT AUTONOMOUS EXPLORATION WITH AUTONOMOUS EXPLORATION

RESULTS: •The estimated and real robot poses differences in both experiments are less than 2%.•The map generated by the filter are similar and consistent for navigation purpose.

NEXT EXPERIMENT:•The solution to SLAM problem is already in progress (hallway of 80 m with some loops situations) to validate the algorithms proposed.

EXAMPLE OF DATA PROCESSING IN FUSION ALGORITHM FOR AN SPECIFIC ROBOT POSE