in this paper the problem of 3d scene and object classification from depth data is addressed. in contrast to high-dimensional feature-based representation, the depth data is described in a low dimensional space. in order to remedy the curse of dimensionality problem, the depth data is described by a sparse model over a learned dictionary. exploiting the algorithmic information theory, a new def...

In this paper, an efficient approach is proposed for loop-closure detection in robot visual SLAM. The method uses mutual information to measure similarity between current view and key frames in an appearance map, and evaluates candidate loop-closure locations in particle filter framework. Specially, the implementation of particle filter is accelerated through updating a set of weight vector of ...

Title : Visual SLAM applications of loop-closure detection Loop-closure detection is crucial for enhancing the robustness of SLAM algorithms in general. For example, after a long travel in unknown terrain, detecting when the robot has returned to a past location makes it possible to increase the accuracy and the consistency of the estimation. Recognizing previously mapped locations can also be ...

This paper introduces vision based loop closure detection in Simultaneous Localisation And Mapping (SLAM) using Tree of Words. The loop closure performance in a complex urban environment is examined and an additional feature is suggested for safer matching. A SLAM ground experiment in an urban area is performed using Tree of Words, a delayed state information filter and planar laser scans for r...

Loop closure detection, the task of identifying locations revisited by a robot in a sequence of odometry and perceptual observations, is typically formulated as a combination of two subtasks: (1) bag-of-words image retrieval and (2) post-verification using RANSAC geometric verification. The main contribution of this study is the proposal of a novel post-verification framework that achieves good...

Visual loop closure detection is an important problem in visual robot navigation. Successful solutions to visual loop closure detection are based on image matching between the current view and the map images. In order to obtain a solution that is scalable to large environments involving thousands or millions of images, the efficiency of a loop closure detection algorithm is critical. Recently p...

We present a simple yet effective method to address loop closure detection in simultaneous localisation and mapping using local 3D deep descriptors (L3Ds). L3Ds are emerging compact representations of patches extracted from point clouds that learnt data learning algorithm. propose novel overlap measure for by computing the metric error between points correspond mutually-nearest-neighbour after ...

This paper addresses the loop closure detection problem in slam, and presents a method for solving the problem using pairwise comparison of point clouds in both 2D and 3D. The point clouds are mathematically described using features that capture important geometric and statistical properties. The features are used as input to the machine learning algorithm AdaBoost, which is used to build a non...