Gene

Quantum mechanics

Robot

Inertial navigation system

Engineering

Extended Kalman filter

Robustness (evolution)

Real-time computing

Telecommunications

Biochemistry

Computer science

Sensor fusion

Geology

Oceanography

Physics

Simulation

Dead reckoning

Global Positioning System

Inertial frame of reference

Artificial intelligence

Kalman filter

Underwater

Chemistry

Inertial measurement unit

Computer vision

IEEE Sensors Journal

Aiming to deal with underwater localization for small-size robots in GPS-denied and structured environment, this paper proposed a novel multi-sensor fusion-based self-localization system using low-cost sensors. Based on multi-sensor information fusion, an Extended Kalman Filter (EKF) is utilized to synthesize the multi-source information from an Inertial Measurement Unit (IMU), optical flow, pressure sensor and ArUco markers, which enables the robot obtain a highly precise positioning. This method also can reduce the location drift over time owing to the loss of markers in pure markers-based localization. Specially, a velocity correction model is proposed using the angle information obtained by IMU, which can compensate optical flow-based velocity estimation errors caused by robot posture changes. Finally, to validate the performance of the proposed self-localization system, simulations are conducted using Gazebo simulator on the robot operating system (ROS). Moreover, a series of experiments in an indoor swimming pool are presented. Results of the proposed method and dead reckoning are compared in simulation and experiment to demonstrate the robustness and feasibility of the proposed localization system.

A Multi-Sensor Fusion Self-Localization System of a Miniature Underwater Robot in Structured and GPS-Denied Environments