Abstract
Data association is one of the key problems in the SLAM community. Several data association failures may cause the SLAM results to be divergent. Data association performance in SLAM is affected by both data association methods and sensor information. Two measures of handling sensor information are introduced herein to enhance data association performance in SLAM. For the first measure, truncating strategy of limited features, instead of all matched features, is used for observation update. These features are selected according to an information variable. This truncating strategy is used to lower the effect of false matched features. For the other measure, a special rejecting mechanism is designed to reject suspected observations. When the predicted robot pose is obviously different from the updated robot pose, all observed sensor information at this moment is discarded. The rejecting mechanism aims at eliminating accidental sensor information. Experimental results indicate that the introduced measures perform well in improving the stability of data association in SLAM. These measures are of extraordinary value for real SLAM applications.
Highlights
The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location [1]
We evaluate the data association results with a rejecting mechanism, which is described in the chapter
Data association is of key importance in the SLAM process
Summary
The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location [1]. The SLAM solution has been seen as the core of the mobile robotics community in the past two decades as it could provide methods to make a robot to be truly autonomous. SLAM data association attracts the attention of many researchers all over the world. Some data association solutions have been proposed in the past decade. As for different sensors, the data association method differs to some extent. A laser range finder and a camera are the two common sensors in SLAM. In the laser range finder, SLAM data association can be divided into two categories: feature-based and scan-based approaches. As for the camera sensor, SLAM data association can be divided into three categories: feature-based methods, appearance-based methods, and hybrid methods
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