This work addresses the problem of the global localization of a mobile robot by exploiting RFID technology. The robot is equipped with kinematic sensors capable of measuring the incremental distances covered by each wheel and an RFID reader which measures the phase of the signal that is backscattered by reference RFID tags situated inside the environment at known positions. The motion of the robot with respect to the tags enables the collection of measurements from multiple antenna-locations, in a synthetic-aperture sense. Localization of the mobile robot over time is accomplished by processing short segments of trajectory during each iteration. The odometry-data are exploited to estimate each segment of the vehicle’s trajectory relative to an unknown initial position. The odometry-based estimation, the known positions of the reference tags, the unwrapped phase measurements and a phase-to-distance model represent the input to a data-fit problem, the solution of which reveals the unknown initial position and orientation of the trajectory segment in the absolute frame. Thanks to phase unwrapping, the data-fit problem features convexity-type properties and the solution is rapidly found by non-linear optimization techniques. The influence of different algorithmic features on the performance of the method is investigated in a simulative context. An experimental campaign that employs a prototype robotic agent equipped with two antennas reports a mean absolute localization error of less than 0.1m, while the execution time stays below the measurements’ collection-time such that the real-time capability of the method is preserved. 

Real-Time Global Localization of a Mobile Robot by Exploiting RFID Technology

Navigation of a mobile robot is conditioned on the knowledge of its pose. In observer-based localisation configurations its initial pose may not be knowable in advance, leading to the need of its estimation. Solutions to the problem of global localisation are either robust against noise and environment arbitrariness but require motion and time, which may (need to) be economised on, or require minimal estimation time but assume environmental structure, may be sensitive to noise, and demand preprocessing and tuning. This article proposes a method that retains the strengths and avoids the weaknesses of the two approaches. The method leverages properties of the Cumulative Absolute Error per Ray metric with respect to the errors of pose estimates of a 2D LIDAR sensor, and utilises scan--to--map-scan matching for fine(r) pose approximations. A large number of tests, in real and simulated conditions, involving disparate environments and sensor properties, illustrate that the proposed method outperforms state-of-the-art methods of both classes of solutions in terms of pose discovery rate and execution time. The source code is available for download. 

CBGL: Fast Monte Carlo Passive Global Localisation of 2D LIDAR Sensor

Recent years have seen the introduction of more affordable but less accurate 2D range sensors whose field of view is $2\pi$. Scan-matching with these has been insufficiently researched, while being a challenge due to these sensors' increased measurement uncertainty. This paper proposes a real-time method for matching scans extracted from panoramic 2D LIDAR sensors. The method leverages properties of the Fourier transform which arise due to the periodicity of the range signal. Matching is performed in a correspondenceless manner. The proposed method outperforms established scan-matching methods in terms of pose accuracy and robustness in tests on public domain data, and over noise levels of commercially available sensors. The source code is available for download.

FSM: Correspondenceless scan-matching of panoramic 2D range scans

Correspondenceless scan-to-map-scan matching of 2D panoramic range scans

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FSM: Correspondenceless scan-matching of panoramic 2D range scans

Correspondenceless scan-to-map-scan matching of 2D panoramic range scans