Monte Carlo localization

Monte Carlo localization (MCL), also known as particle filter localization, is an algorithm for robots to localize using a particle filter. Given a map of the environment, the algorithm estimates the position and orientation of a robot as it moves and senses the environment. The algorithm uses a particle filter to represent the distribution of likely states, with each particle representing a possible state, i.e., a hypothesis of where the robot is. The algorithm typically starts with a uniform random distribution of particles over the configuration space, meaning the robot has no information about where it is and assumes it is equally likely to be at any point in space. Whenever the robot moves, it shifts the particles to predict its new state after the movement. Whenever the robot senses something, the particles are resampled based on recursive Bayesian estimation, i.e., how well the actual sensed data correlate with the predicted state. Ultimately, the particles should converge towards the actual position of the robot. Consider a robot with an internal map of its environment. When the robot moves around, it needs to know where it is within this map. Determining its location and rotation (more generally, the pose) by using its sensor observations is known as robot localization. Because the robot may not always behave in a perfectly predictable way, it generates many random guesses of where it is going to be next. These guesses are known as particles. Each particle contains a full description of a possible future state. When the robot observes the environment, it discards particles inconsistent with this observation, and generates more particles close to those that appear consistent. In the end, hopefully most particles converge to where the robot actually is. The state of the robot depends on the application and design. For example, the state of a typical 2D robot may consist of a tuple for position and orientation . For a robotic arm with 10 joints, it may be a tuple containing the angle at each joint: .

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MICRO-452: Basics of mobile robotics

The course teaches the basics of autonomous mobile robots. Both hardware (energy, locomotion, sensors) and software (signal processing, control, localization, trajectory planning, high-level control)

Localization of inexpensive robots with low-bandwidth sensors

Francesco Mondada, Stéphane Magnenat, Ming Liu, Shiling Wang

Recent progress in electronics has allowed the construction of affordable mobile robots. This opens many new opportunities, in particular in the context of collective robotics. However, while several algorithms in this field require global localization, this capability is not yet available in low-cost robots without external electronics. In this paper, we propose a solution to this problem, using only approximate dead-reckoning and infrared sensors measuring the grayscale intensity of a known visual pattern on the ground. Our approach builds on a recursive Bayesian filter, of which we demonstrate two implementations: a dense Markov Localization and a particle-based Monte Carlo Localization. We show that both implementations allow accurate localization on a large variety of patterns, from pseudo-random black and white matrices to grayscale images. We provide a theoretical estimate and an empirical validation of the necessary traveled distance for convergence. We demonstrate the real-time localization of a Thymio II robot. These results show that our system solves the problem of absolute localization of inexpensive robots. This provides a solid base on which to build navigation or behavioural algorithms.

SPRINGER INTERNATIONAL PUBLISHING AG2019