A review of feature detection and match algorithms for localization and mapping

Simultaneous localization and mapping (SLAM) is a technique used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. An important component of SLAM is feature extraction, which is the process of detecting and extracting significant features such as corners, edges, and walls in an environment. Here, the use of sonars as sensors mounted on a mobile platform is examined, and a comparison of different algorithms currently in use is made and presented. This comparison is performed through a combination of experimental and numerical studies. The triangulation-based fusion algorithm is examined for point feature detection, and the standard Hough Transform and the triangulation Hough fusion (THF) are used for line detection. Comparisons are discussed and presented along with ongoing work.

Download Full-text

Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2010.p0140 ◽

2010 ◽

Vol 22 (2) ◽

pp. 140-149 ◽

Cited By ~ 7

Author(s):

Atsushi Sakai ◽

◽

Teppei Saitoh ◽

Yoji Kuroda

Keyword(s):

Data Management ◽

Feature Detection ◽

Data Association ◽

Position Estimation ◽

Outdoor Environment ◽

Unscented Transformation ◽

Particle Sampling ◽

Localization And Mapping ◽

Outdoor Environments ◽

Stable Feature

In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.

Download Full-text

Autonomous planetary rover navigation via active SLAM

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-12-2016-0239 ◽

2018 ◽

Vol 91 (1) ◽

pp. 60-68

Author(s):

Guoqing Li ◽

Yunhai Geng ◽

Wenzheng Zhang

Keyword(s):

Feature Detection ◽

Three Dimensional ◽

Salient Feature ◽

Efficient Manner ◽

Planetary Rover ◽

Content Type ◽

Feature Extraction Method ◽

Localization And Mapping ◽

Salient Features ◽

Exploration Mission

PurposeThis paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.Design/methodology/approachA three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.FindingsThe proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.Originality/valueThis paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

Download Full-text

Autonomous Salient Feature Detection through Salient Cues in an HSV Color Space for Visual Indoor Simultaneous Localization and Mapping

Advanced Robotics ◽

10.1163/016918610x512613 ◽

2010 ◽

Vol 24 (11) ◽

pp. 1595-1613 ◽

Cited By ~ 13

Author(s):

Yong-Ju Lee ◽

Jae-Bok Song

Keyword(s):

Feature Detection ◽

Color Space ◽

Simultaneous Localization And Mapping ◽

Salient Feature ◽

Hsv Color Space ◽

Localization And Mapping

Download Full-text

Vision and laser fused SLAM in indoor environments with multi-robot system

Assembly Automation ◽

10.1108/aa-04-2018-065 ◽

2019 ◽

Vol 39 (2) ◽

pp. 297-307 ◽

Cited By ~ 6

Author(s):

Haoyao Chen ◽

Hailin Huang ◽

Ye Qin ◽

Yanjie Li ◽

Yunhui Liu

Keyword(s):

Large Scale ◽

Feature Detection ◽

Mobile Robotics ◽

Least Square ◽

Indoor Environments ◽

Fusion Algorithm ◽

Content Type ◽

Localization And Mapping ◽

Loop Detection ◽

Multi Robot

Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

Download Full-text

Topological simultaneous localization and mapping: a survey

Robotica ◽

10.1017/s0263574713001070 ◽

2013 ◽

Vol 32 (5) ◽

pp. 803-821 ◽

Cited By ~ 20

Author(s):

Jaime Boal ◽

Álvaro Sánchez-Miralles ◽

Álvaro Arranz

Keyword(s):

Feature Detection ◽

Simultaneous Localization And Mapping ◽

Map Matching ◽

Unstructured Environments ◽

Localization And Mapping ◽

Viable Solution ◽

Introductory Overview

SUMMARYOne of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.

Download Full-text

Point-Line Visual Stereo SLAM Using EDlines and PL-BoW

Remote Sensing ◽

10.3390/rs13183591 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3591

Author(s):

Hanxiao Rong ◽

Yanbin Gao ◽

Lianwu Guan ◽

Alex Ramirez-Serrano ◽

Xu Xu ◽

...

Keyword(s):

Feature Detection ◽

Geometric Constraints ◽

Spatial Proximity ◽

Visual Words ◽

Model Combining ◽

Localization And Mapping ◽

Loop Detection ◽

Bow Technique ◽

Line Features ◽

Point Line

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

Download Full-text