Probabilistic Scan Matching Research Articles

Indoor Global Localization Using Depth-Guided Photometric Edge Descriptor for Mobile Robot Navigation

This paper suggests a new landmark descriptor for indoor mobile robot navigation with sensor fusion and a global localization method using it. In previous research on robot pose estimation, various landmarks such as geometric features, visual local-invariant features, or objects are utilized. However, in real-world situations, there is a possibility that distinctive landmarks are insufficient or there are many similar landmarks repeated in indoor environment, which makes accurate pose estimation difficult. In this work, we suggest a new landmark descriptor, called depth-guided photometric edge descriptor (DPED), which is composed of superpixels and approximated 3D depth information of photometric vertical edge. With this descriptor, we propose a global localization method based on coarse-to-fine strategy. In the coarse step, candidate nodes are found by place recognition using our pairwise constraint-based spectral matching technique, and the robot pose is estimated with a probabilistic scan matching in the fine step. The experimental results show that our method successfully estimates the robot pose in the real-world tests even when there is a lack of distinctive features and objects.

The UspIC: Performing Scan Matching Localization Using an Imaging Sonar

This paper presents a novel approach to localize an underwater mobile robot based on scan matching using a Mechanically Scanned Imaging Sonar (MSIS). When used to perform scan matching, this sensor presents some problems such as significant uncertainty in the measurements or large scan times, which lead to a motion induced distortion. This paper presents the uspIC, which deals with these problems by adopting a probabilistic scan matching strategy and by defining a method to strongly alleviate the motion induced distortion. Experimental results evaluating our approach and comparing it to previously existing methods are provided.

신뢰성 높은 위치 인식을 위하여 방향을 고려한 확률적 스캔 매칭 기법

The scan matching is widely used in localization and mapping of mobile robots. This paper presents a probabilistic scan matching method. To improve the performance of the scan matching, a direction of data point is incorporated into the scan matching. The direction of data point is calculated using the line fitted by the neighborhood data. Owing to the incorporation, the performance of the matching was improved. The number of iterations in the scan matching decreased, and the tolerance against a high rotation between scans increased. Based on real data of a laser range finder, experiments verified the performance of the proposed direction augmented probabilistic scan matching algorithm.

Object-spatial layout-route based hybrid map and global localization for mobile robots

This paper presents new object-spatial layout-route based hybrid map representation and global localization approaches using a stereo camera. By representing objects as high-level features in a map, a robot can deal more effectively with different contexts such as dynamic environments, human-robot interaction, and semantic information. However, the use of objects alone for map representation has inherent problems. For example, it is difficult to represent empty spaces for robot navigation, and objects are limited to readily recognizable things. One way to overcome these problems is to develop a hybrid map that includes objects and the spatial layout of a local space. The map developed in this research has a hybrid structure that combines a global topological map and a local hybrid map. The topological map represents the spatial relationships between local spaces. The local hybrid map combines the spatial layout of the local space with the objects found in that space. Based on the proposed map, we suggest a novel coarse-to-fine global localization method that uses object recognition, point cloud fitting and probabilistic scan matching. This approach can accurately estimate robot pose with respect to the correct local space.

MSISpIC: a probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper compares two well known scan matching algorithms: the MbICP and the pIC. As a result of the study, it is proposed the MSISpIC, a probabilistic scan matching algorithm for the localization of an Autonomous Underwater Vehicle (AUV). The technique uses range scans gathered with a Mechanical Scanning Imaging Sonar (MSIS), and the robot displacement estimated through dead-reckoning with the help of a Doppler Velocity Log (DVL) and a Motion Reference Unit (MRU). The proposed method is an extension of the pIC algorithm. Its major contribution consists in: 1) using an EKF to estimate the local path traveled by the robot while grabbing the scan as well as its uncertainty and 2) proposing a method to group into a unique scan, with a convenient uncertainty model, all the data grabbed along the path described by the robot. The algorithm has been tested on an AUV guided along a 600m path within a marina environment with satisfactory results.

A Probabilistic Framework for Sonar Scan Matching Localization

Scan matching is a popular localization technique based on comparing two sets of range readings gathered at consecutive robot poses. Scan matching algorithms implicitly assume that matching readings correspond to the same object in the environment. This is a reasonable assumption when using accurate sensors such as laser range finders and that is why they are extensively used to perform scan matching localization. However, when using other sensors such as ultrasonic range finders or visual sonar, this assumption is no longer valid because of their lower angular resolution and the sparsity of the readings. In this paper we present a sonar scan matching framework, the spIC, which is able to deal with the sparseness and low angular resolution of sonar sensors. To deal with sparseness, a process to group sonar readings gathered along short robot trajectories is presented. Probabilistic models of ultrasonic and odometric sensors are defined to cope with the low sonar angular resolution. Consequently, a probabilistic scan matching process is performed. Finally, the correction of the whole robot trajectory involved in the matching process is presented as a constrained optimization problem.