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빠른 루프 클로징을 위한 2D포즈 노드 샘플링 휴리스틱
이재준(Jae-Jun Lee),유지환(Jee-Hwan Ryu) 제어로봇시스템학회 2016 제어·로봇·시스템학회 논문지 Vol.22 No.12
The graph-based SLAM (Simultaneous Localization and Mapping) approach has been gaining much attention in SLAM research recently thanks to its ability to provide better maps and full trajectory estimations when compared to the filtering-based SLAM approach. Even though graph-based SLAM requires batch processing causing it to be computationally heavy, recent advancements in optimization and computing power enable it to run fast enough to be used in real-time. However, data association problems still require large amount of computation when building a pose graph. For example, to find loop closures it is necessary to consider the whole history of the robot trajectory and sensor data within the confident range. As a pose graph grows, the number of candidates to be searched also grows. It makes searching the loop closures a bottleneck when solving the SLAM problem. Our approach to alleviate this bottleneck is to sample a limited number of pose nodes in which loop closures are searched. We propose a heuristic for sampling pose nodes that are most advantageous to closing loops by providing a way of ranking pose nodes in order of usefulness for closing loops.
2D Pose Nodes Sampling Heuristic For Fast Loop Closing
Syed Zain Mehdi,Jee-Hwan Ryu 한국자동차공학회 2017 한국자동차공학회 지부 학술대회 논문집 Vol.2017 No.6
Graph SLAM approach has been gaining much attention in SLAM research recently thanks to its ability to provide better map and full trajectory estimation when compared to filter based SLAM approaches. Even though graph SLAM requires batch processing it to be comparatively computationally expensive, recent advancements in optimization and computing power enable it to run fast enough to be used even in real-time. However, data association problem still requires much of computation when building a pose graph. For example, to find loop closures it is necessary to consider the whole history of robot trajectory and sensor data within the confident range. As a pose graph grows, the number of candidates to be searched also grows. It makes searching the loop closures a bottleneck in SLAM algorithm. Our approach to alleviate this bottleneck is to sample limited number of pose nodes in which loop closures are searched. We propose a heuristic for sampling pose nodes that are most advantageous to closing loops by providing a way of ranking pose nodes in order of usefulness.
Adaptive Sliding Window for Hierarchical Pose-Graph-Based SLAM
Seungwook Lim,Tae-kyeong Lee,Seongsoo Lee,Shounan An,Se-young Oh 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
We propose the Adaptive Sliding Window (ASW) which is a novel approach to solve the hierarchical pose-graph-based (PGB) simultaneous localization and mapping (SLAM) problem. We adjust the size of the sliding window (SW) for incremental optimization by eliminating the portion of the graph which has a low degree of similarity to the rest of the graph and by dropping poses which are not related to the latest robot pose. The decision is made by utilizing a graph-cut algorithm, where the weight matrix is created from the constraints’ information matrices estimated by the front-end system. Our method provides the optimal window size to minimize information loss and linearization error. Moreover, due to the optimal SW size, our method produces the additional advantage of constructing an efficient hierarchical structure. To make a high-level graph, we create a high-level node (local map) by immobilizing the truncated part from the SW. The local maps can be efficiently matched in the front-end system to estimate the constraints between the high-level nodes. Therefore, our approach increases localization accuracy. We tested our algorithm on the indoor dataset obtained in an apartment environment to demonstrate the effectiveness of the proposed method. When our approach was applied to the hierarchical PGB SLAM back-end, we efficiently improved both localization accuracy (by reducing the information loss) and computational efficiency simultaneously.
권대현,김주완,김문환,박호규,김태영,김아영,Gwon, Dae-Hyeon,Kim, Joowan,Kim, Moon Hwan,Park, Ho Gyu,Kim, Tae Yeong,Kim, Ayoung 한국로봇학회 2017 로봇학회 논문지 Vol.12 No.4
Side scanning sonar (SSS) provides valuable information for robot navigation. However using the side scanning sonar images in the navigation was not fully studied. In this paper, we use range data, and side scanning sonar images from UnderWater Simulator (UWSim) and propose measurement models in a feature based simultaneous localization and mapping (SLAM) framework. The range data is obtained by echosounder and sidescanning sonar images from side scan sonar module for UWSim. For the feature, we used the A-KAZE feature for the SSS image matching and adjusting the relative robot pose by SSS bundle adjustment (BA) with Ceres solver. We use BA for the loop closure constraint of pose-graph SLAM. We used the Incremental Smoothing and Mapping (iSAM) to optimize the graph. The optimized trajectory was compared against the dead reckoning (DR).
Simultaneous Localization and Mapping in the Epoch of Semantics: A Survey
Muhammad Sualeh,김곤우 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.3
Simultaneous Localization and Mapping (SLAM) with an astonishing research history of over threedecades has brought the concept to the door step of truly autonomous robotic systems. The concept has advancedbeyond the map building and self-localization of robot on the map. On the other hand, the long-standing challengespertaining to the provision of out of the box solution for range of conditions still needs to be addressed. However, thetechnological advancements in the area is steadily making its ways into industry. This paper surveys state-of-the-artSLAM and discuss the insights of existing methods. Starting with a classical definition of SLAM, a brief conceptualoverview, and formulation of a standard SLAM system is carried out. While discussing the auxiliaries for solvingSLAM, the influx of machine learning into SLAM is also addressed. Moreover, recent SLAM algorithms havebeen reviewed with a focus on emerging concept of semantics to augment the system. In this paper a taxonomyof recently developed SLAM algorithms with a detailed comparison metrics, is presented. Furthermore, openchallenges, future directions and emerging research issues have also been laid down.