Matching Risk for Feature Selection in Visual SLAM

An Integrated Feature Selection Strategy for Monocular Slam

For feature-based monocular bearing-only SLAM, how to select useful features for SLAM process is crucial. The reason is overwhelming feature number will not only seriously slow down the system but produce erroneous SLAM result due to feature mismatching. In this paper, we propose a novel method for feature selection. The method combines both bottom-up (visual saliency) and top-down (learned obj...

Fast Template Matching of Objects for Visual SLAM

The majority of visual SLAM techniques utilize interest points as landmarks. Therefore, they suffer from two main problems; scalability and data association reliability. Recently, there has been increasing interest in using higher level object description to reduce the number of tracked features and improve the data association among frames. In this paper, a simple visual mono SLAM algorithm is...

Efficient Feature Parameterisation for Visual SLAM Using Inverse Depth Bundles

Flexibility and robustness of visual SLAM systems have been shown to benefit from an inverse depth parameterisation of features. However the increased number of 6 parameters per feature presents a problem to real-time EKF SLAM implementations because their computational complexity scales quadratically with the size of the state vector. Recent work tackles this for instance by converting the rep...

The case for visual-inertial SLAM over visual SLAM

Learning and Feature Selection in Stereo Matching

We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we...