ICRA 2011 Paper Abstract


Paper ThP110.5

Yap, Teddy Jr. (University of California, Riverside), Li, Mingyang (University of California, Riverside), Mourikis, Anastasios (University of California, Riverside), Shelton, Christian (University of California at Riverside)

A Particle Filter for Monocular Vision-Aided Odometry

Scheduled for presentation during the Regular Sessions "Localization III" (ThP110), Thursday, May 12, 2011, 14:40−14:55, Room 5E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on August 19, 2019

Keywords Localization, Sensor Fusion, Autonomous Navigation


We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observations of naturally occurring static point features in the environment. A key contribution of this work is a novel approach for computing the particle weights, which does not require including the feature positions in the state vector. As a result, the computational and sample complexities of the algorithm remain low even in feature-dense environments. We validate the effectiveness of the approach extensively with both simulations as well as real-world data, and compare its performance against that of the extended Kalman filter (EKF) and FastSLAM. Results from the simulation tests show that the particle filter approach is better than these competing approaches in terms of the RMS error. Moreover, the experiments demonstrate that the approach is capable of achieving good localization accuracy in complex environments.



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