Paper Info
Title
The importance of measurement uncertainty modelling in the feature-based RGB-D SLAM
Abstract
This paper presents an analysis of the role of measurements uncertainty in the feature-based RGB-D SLAM formulated as graph optimization problem. The considered SLAM solution uses global graph optimization to find the trajectory of the RGB-D camera and a set of 3D point features constituting the map. In order to focus on the optimization back-end details and isolate the results from the data association errors caused by the image processing front-end in a real SLAM system we introduce a simulation environment, which allows to clearly show the influence of the uncertainty model on the accuracy of the obtained trajectories. We demonstrate a substantial improvement in the trajectory accuracy due to using in the graph optimization process an uncertainty model based on the physical properties of the RGB-D sensor. Moreover, we investigate the influence of the RGB-D camera motion strategy on the accuracy of the SLAM solution, pointing out the relation between this strategy and the measurement uncertainty model.
Year
DOI
Venue
2015
10.1109/RoMoCo.2015.7219752
2015 10th International Workshop on Robot Motion and Control (RoMoCo)
Keywords
Field
DocType
measurement uncertainty modelling,feature-based RGB-D SLAM,graph optimization problem,SLAM solution,global graph optimization,3D point feature,data association error,image processing front-end,real SLAM system,simulation environment,trajectory accuracy,graph optimization process,RGB-D sensor,RGB-D camera motion strategy
Computer vision,Uncertainty model,Measurement uncertainty,Image processing,Data association,RGB color model,Artificial intelligence,Feature based,Simultaneous localization and mapping,Trajectory,Mathematics
Conference
Citations 
PageRank 
References 
0
0.34
20
Authors
2
Name
Order
Citations
PageRank
Dominik Belter110016.31
Piotr Skrzypczynski214825.07