Research Article Camera Space Particle Filter for the Robust and Precise Indoor Localization of a Wheelchair Raul Chavez-Romero, 1 Antonio Cardenas, 1 Mauro Maya, 1 Alejandra Sanchez, 1 and Davide Piovesan 2 1 Universidad Autonoma de San Luis Potosi, 78290 San Luis Potosi, SLP, Mexico 2 Gannon University, Erie, PA 16541, USA Correspondence should be addressed to Raul Chavez-Romero; raulcr2010@gmail.com Received 19 December 2014; Accepted 25 March 2015 Academic Editor: Changhai Ru Copyright © 2016 Raul Chavez-Romero et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper presents the theoretical development and experimental implementation of a sensing technique for the robust and precise localization of a robotic wheelchair. Estimates of the vehicle’s position and orientation are obtained, based on camera observations of visual markers located at discrete positions within the environment. A novel implementation of a particle flter on camera sensor space (Camera-Space Particle Filter) is used to combine visual observations with sensed wheel rotations mapped onto a camera space through an observation function. Te camera space particle flter fuses the odometry and vision sensors information within camera space, resulting in a precise update of the wheelchair’s pose. Using this approach, an inexpensive implementation on an electric wheelchair is presented. Experimental results within three structured scenarios and comparative performance using an Extended Kalman Filter (EKF) and Camera-Space Particle Filter (CSPF) implementations are discussed. Te CSPF was found to be more precise in the pose of the wheelchair than the EKF since the former does not require the assumption of a linear system afected by zero-mean Gaussian noise. Furthermore, the time for computational processing for both implementations is of the same order of magnitude. 1. Introduction Recently, the use of diverse types of sensors and diferent strategies for information fusion has allowed important developments in key areas of robotic and artifcial intelli- gence. Within these disciplines, a specifc area of investiga- tion is mobile robotics where the sensor-based localization problem is an important research topic. Localization of an autonomous mobile robot is the main concern of a navigation strategy since it is necessary to know precisely the actual position of the mobile robot in order to apply a control law or execute a desired task. In general, a navigation system requires a set of sensors and a fusion algorithm that integrates the sensors information to reliably estimate the pose of the mobile robot. One of the most commonly used sensors in wheeled mobile robots is odometers (dead reckoning). Unfortunately, these sensors are subjected to accumulated errors introduced by wheel slippage or other uncertainties that may perturb the course of the robot. Terefore, odo- metric estimations need to be corrected by a complementary type of sensor. Reported works in autonomous robots present approaches where the odometry sensors information is com- plemented with diferent types of sensors such as ultrasonic sensor [1–3], LIDAR (Light Detection and Ranging) [4–8], digital cameras [9–12], magnetic feld sensor [13], global position system (GPS) [7, 8, 14, 15], and Inertia Measurement Units (IMUs) [7, 15]. Among the diferent types of sensors there exist advan- tages and drawbacks depending on the general application of the mobile robots considered. GPS systems are low-cost systems and relatively easy to implement but have low accu- racy and their use is not convenient for indoor environments. IMUs are relatively inexpensive, easy to implement, and ef- cient for outdoor and indoor conditions but are very sensitive to vibration-like noise and are not convenient for precise applications. LIDAR sensors have high accuracy and are Hindawi Publishing Corporation Journal of Sensors Volume 2016, Article ID 8729895, 11 pages http://dx.doi.org/10.1155/2016/8729895