A noise-robust obstacle detection algorithm for mobile robots using active 3D sensors

Obstacle detection is one of the most important tasks for mobile robots moving along a plane, and it is critical to avoid damages, either to the robot or to human operators. In the past decades, several techniques were proposed for visual navigation of mobile robots, relying on different kind of sensors and algorithms: sonar sensors, laser stripes, and stereo vision are commonly used techniques. Even if these techniques are well-established and used in commercial robots, different and better sensors are now widespread, such as depth sensors. This work proposes an algorithm based on the use of an active 3D depth sensor for obstacle detection and avoidance. The algorithm, conceived to be used in embedded systems with low processing power, underwent several experiments and proved to be robust to Gaussian white noise.