Having a robot figure out its global position is required in many real world applications, it’s also one of the biggest challenges in robotics.

The easiest approach is to have a robot blindly keep track of its movements (odometry) from a known starting position. Odometry alone however quickly results in an add-up of errors that make the localization unusable.

To help the robot along the way, Boccadoro et al. propose to place passive Radio-Frequency IDentification (RFID) tags in known positions in the environment. These smart tags are interesting because they are typically low cost and require no energy to function. Robots equipped with RFID readers can detect a tag within a 1m range, although with a lot of noise. Algorithms are then needed to combine the robot’s sensors, in this case odometry, with the noisy RFID readings to precisely estimate its global position.

For this purpose, two types of Kalman Filters are implemented and compared to a Particle Filter method that typically has much larger computational cost. Experiments were conducted using a Pioneer P3-DX driving around a corridor equipped with 6 RFID tags.

Results show that the first method is fast but imprecise when tags are sparse (figure left). The second approach has higher computation requirements than the first but is able to obtain estimates as good as the Particle Filter method with few tags (figure right).

The path reconstructed through the various methods proposed: a red line is used to represent the estimation of the second loop of the robot path, the green line is used for the last loop; the line in blue is ground truth.

In the future, authors hope to investigate the optimal placement of RFID tags to achieve even better position estimates.