Local Grid Map Research Articles

위상학적 공간 인식을 위한 효과적인 초음파 격자 지도 매칭 기법 개발

This paper presents a method of sonar grid map matching for topological place recognition. The proposed method provides an effective rotation invariant grid map matching method. A template grid map is firstly extracted for reliable grid map matching by filtering noisy data in local grid map. Using the template grid map, the rotation invariant grid map matching is performed by Ring Projection Transformation. The rotation invariant grid map matching selects candidate locations which are regarded as representative point for each node. Then, the topological place recognition is achieved by calculating matching probability based on the candidate location. The matching probability is acquired by using both rotation invariant grid map matching and the matching of distance and angle vectors. The proposed method can provide a successful matching even under rotation changes between grid maps. Moreover, the matching probability gives a reliable result for topological place recognition. The performance of the proposed method is verified by experimental results in a real home environment.

초음파 격자 지도를 이용한 위상학적 지도 작성 기법 개발

This paper presents a method of topological modeling using only low-cost sonar sensors. The proposed method constructs a topological model by extracting sub-regions from the local grid map. The extracted sub-regions are considered as nodes in the topological model, and the corresponding edges are generated according to the connectivity between two sub-regions. A grid confidence for each occupied grid is evaluated to obtain reliable regions in the local grid map by filtering out noisy data. Moreover, a convexity measure is used to extract sub-regions automatically. Through these processes, the topological model is constructed without predefining the number of sub-regions in advance and the proposed method guarantees the convexity of extracted sub-regions. Unlike previous topological modeling methods which are appropriate to the corridor-like environment, the proposed method can give a reliable topological modeling in a home environment even under the noisy sonar data. The performance of the proposed method is verified by experimental results in a real home environment.

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.

Obstacle Detection for Patrol Robot Using Bumblebee2 Stereo Vision System

This paper employs Bumblebee2 stereo vision system to detect the obstacles for patrol robot in substation environment. Firstly, with the selected points in the disparity image, the ground plane is calculated by the RANSAC (Random Sample Consensus) algorithm. And then, the local occupancy grid map is built for patrol robot, and the obstacles are detected through connected component analysis method. The actual test in substation environment verified the reliability of the system.

Metric-topological maps from laser scans adjusted with incremental tree network optimizer

Several adaptations of maximum likelihood approaches to incremental map learning have been proposed recently. In particular, an incremental network optimizer based on stochastic gradient descent provides a fast and easy-to-implement solution to the problem. In this paper, we illustrate two map builders that process laser scans in order to extract the constraint network for the optimization algorithm. The first algorithm builds a map in the form of a collection of scans corresponding to a subset of the poses of a robot moving in the environment. Even though such a solution has the advantage of simplicity, it requires careful processing of data associations. After a preliminary selection of pose constraints candidates, a relative pose is computed through standard scan matching techniques. The second map builder stores a hybrid metric-topological representation: the map consists of a graph whose nodes contain local occupancy grid maps and whose edges are labeled with the relative pose between pairs of nodes. Each patch map summarizes the information of consecutive raw scans and such a richer representation better solves loop closure. Association between pairs of local maps is then performed and tested using correlation-based techniques. Our aim is to illustrate the effectiveness of a tree network optimizer integrated with simple methods for data association. Experiments reported in the paper show that a compact system integrating the optimizer and one of two versions of the map builder works reasonably well with commonly used benchmarks.

Real-time building of a thinning-based topological map

An accurate and compact map is essential to an autonomous mobile robot system. A topological map, one of the most popular map types, can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map by using a thinning algorithm. This algorithm, when combined with the application of the C-obstacle, can easily extract only the meaningful topological information in real-time and is robust to environment change, because this map is extracted from a local grid map generated based on the Bayesian update formula. In this paper, position probability is defined to evaluate the quantitative reliability of the end node extracted by the thinning process. Since the thinning process builds only local topological maps, a global topological map should be constructed by merging local topological maps according to nodes with high position probability. For real and complex environments, experiments showed that the proposed map building method based on the thinning process can accurately build a local topological map in real-time, with which an accurate global topological map can be incrementally constructed.

Thinning-Based Topological Exploration Using Position Possibility of Topological Nodes

A grid map can be efficiently used in navigation, but this type of map requires a large amount of memory in proportion to the size of the environment. As an alternative, a topological map can be used to represent the environment in terms of discrete nodes with edges connecting them. It is usually constructed by Voronoi-like graphs, but in this paper the topological map is built based on the local grid map by using a thinning algorithm. This new approach can easily extract the topological information in real-time and be robustly applicable to the real environment, and this map can be autonomously built by exploration. The position possibility is defined to evaluate the quantitative reliability of the topological map and then a new exploration scheme based on the position possibility is proposed. From the position possibility information, the robot can determine whether or not it needs to visit a specific end node, which node will be the next target and how much of the environment has yet been explored. Various experiments showed that the proposed map-building and exploration methods can accurately build a local topological map in real-time and can guide a robot safely even in a dynamic environment.

지역 및 전역 환경에 대한 세선화 기반 위상지도의 작성

An accurate and compact map is essential to an autonomous mobile robot system. For navigation, it is efficient to use an occupancy grid map because the environment is represented by probability distribution. But it is difficult to apply it to the large environment since it needs a large amount of memory proportional to the environment size. As an alternative, a topological map can be used to represent it in terms of the discrete nodes with edges connecting them. It is usually constructed by the Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map using the thinning algorithm. This algorithm can extract only meaningful topological information by using the C-obstacle concept in real-time and is robust to the environment change, because its underlying local grid map is constructed based on the Bayesian update formula. In this paper, the position probability is defined to evaluate the quantitative reliability of the end nodes of this thinning-based topological map (TTM). The global TTM can be constructed by merging each local TTM by matching the reliable end nodes determined by the position probability. It is shown that the proposed TTM can represent the environment accurately in real-time and it is readily extended to the global TTM.

Range sensor based outdoor vehicle Navigation, collision avoidance and parallel parking

Detecting unexpected obstacles and avoiding collisions is an important task for any autonomous mobile system. This article describes GANESHA (Grid based Approach for Navigation by Evidence Storage and Histogram Analysis), a system using sonar that we implemented for the autonomous land vehicle Navlab. The general hardware configuration of the system is shown, followed by a description of how the system builds a local grid map of its environment. The information collected in the map can then be used for a variety of applications in vehicle navigation like collision avoidance, feature tracking and parking. An algorithm was implemented that can track a static feature such as a rail, wall or an array of parked cars and use this information to drive the vehicle. Methods for filtering the raw data and generating the steering commands are discussed and the implementation for collision avoidance, parallel parking and its integration with other vehicle systems is described.