Line Landmarks Research Articles

Position correction of mobile robot by detecting line landmark using Hough line transform

Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.

A Robust 2D Lidar SLAM Method in Complex Environment

The two-dimensional (2D) lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment. We propose a robust 2D lidar simultaneous localization and mapping (SLAM) algorithm working in ambiguous environments. To improve the front-end scan-matching module’s accuracy and robustness, we propose performing degeneration analysis, line landmark tracking, and environment coverage analysis. The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment. Moreover, we propose a tightly coupled framework that integrates lidar, wheel odometer, and inertial measurement unit (IMU). The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem. The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8% of the existing method. And it can run stably in ambiguous environments where the existing method will be failed. Moreover, it ccan successfully construct a map with an area of more than 250 000 square meters.

SLC-VIO: a stereo visual-inertial odometry based on structural lines and points belonging to lines

AbstractTo improve mobile robot positioning accuracy in building environments and construct structural three-dimensional (3D) maps, this paper proposes a stereo visual-inertial odometry (VIO) system based on structural lines and points belonging to lines. The 2-degree-of-freedom (DoF) spatial structural lines based on the Manhattan world assumption are used to establish visual measurement constraints. The property of point belonging to a line (PPBL) is used to initialize the structural lines and establish spatial distance-residual constraints between point and line landmarks in the reconstructed 3D map. Compared with the 4-DoF spatial straight line, the 2-DoF structural line reduces the variables to be estimated and introduces the orientation information of scenes to the VIO system. The utilization of PPBL makes the proposed system fully exploit the prior geometric information of environments and then achieves better performance. Tests on public data sets and real-world experiments show that the proposed system can achieve higher positioning accuracy and construct 3D maps that better reflect the structure of scenes than existing VIO approaches.

A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots

This paper presents a new implementation method for efficient simultaneous localization and mapping using a forward-viewing monocular vision sensor. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots. In this paper, the orientation of a robot is directly estimated using the direction of the vanishing point. Then, the estimation models for the robot position and the line landmark are derived as simple linear equations. Using these models, the camera poses and landmark positions are efficiently corrected by a local map correction method. The performance of the proposed method is demonstrated under various challenging environments using dataset-based experiments using a desktop computer and real-time experiments using a low-cost embedded system. The experimental environments include a real home-like setting. These conditions contain low-textured areas, moving people, or changing environments. The proposed method is also tested using the robotics advancement through web publishing of sensorial and elaborated extensive datasets benchmark dataset.

Visual EKF-SLAM from Heterogeneous Landmarks.

Many applications require the localization of a moving object, e.g., a robot, using sensory data acquired from embedded devices. Simultaneous localization and mapping from vision performs both the spatial and temporal fusion of these data on a map when a camera moves in an unknown environment. Such a SLAM process executes two interleaved functions: the front-end detects and tracks features from images, while the back-end interprets features as landmark observations and estimates both the landmarks and the robot positions with respect to a selected reference frame. This paper describes a complete visual SLAM solution, combining both point and line landmarks on a single map. The proposed method has an impact on both the back-end and the front-end. The contributions comprehend the use of heterogeneous landmark-based EKF-SLAM (the management of a map composed of both point and line landmarks); from this perspective, the comparison between landmark parametrizations and the evaluation of how the heterogeneity improves the accuracy on the camera localization, the development of a front-end active-search process for linear landmarks integrated into SLAM and the experimentation methodology.

Research on Advanced Algorithm of Angle Bisector Line Landmarks Labeling

According to the characteristics of mobile navigation based on PDA, the map scenes in the screens should be changed in real time, which needs to dynamically output map labels to adapt to these challenges. In this paper, based on angle bisector advanced algorithm is used for labeling dynamic on line water system and obtaining smooth location of line features as the location of labels. The results obtained in this paper can be applied to solve the problems of ambiguous expressions and/or invisibility of labels of line features, achieving the unity of the integrity and aesthetics of the automated output.

Research on the Location of Parallel Line Labeling Based on Water System of Line Landmarks

The line features are mainly labeled by center line labeling mode, parallel line labeling mode and point labeling mode. These three labeling modes have different needs. In this paper, how to choose the best position labeling is discussed, the location of parallel line labeling is used for dividing dynamic segments based on line labels and obtaining smooth location of line features as the location of labels. The results obtained in this paper can be applied to solve the problems of ambiguous expressions and/or invisibility of labels of line features, achieving the unity of the integrity and aesthetics of the automated output.

SLAM and data fusion from visual landmarks and 3D planes

Structured environment can be modelled in a simplified way as a set of planar surfaces and lines. For mobile robot equipped with a 3D sensor and a camera, the incremental construction of such a model is a Simultaneous Localisation And Mapping (SLAM) problem: while exploring the environment, the robot executes motions; from each position, it acquires sensory data, extracts 3D perceptual features, and simultaneously, performs self-localisation and model update. Our robot JIDO has a 3D pivoting laser range finder, acquiring images of 3D points, and has a camera. Firstly, a segmentation algorithm of a 3D image into a set of planar faces is described: this algorithm uses a region growing strategy and the Extended Kalman Filtering to estimate the parameters of the support plane of every face. These planar faces are used as landmarks. Next, we describe how to extract 2D line landmarks by fusing data from both sensors. Our stochastic map is of heterogeneous type and contains plane and 2D line landmarks. At first, The SLAM formalism is used to build a stochastic planar map, and results on the incremental construction of such a map are presented, further on, heterogeneous map will be constructed.

移動ロボットのためのランドマーク観測計画

Recognition of the current location is important for a mobile robot to follow the planned path. Landmarks, which are assumed in the environment around the robot, are used to support this recognition. A mobile robot basically estimates its current location by dead-reckoning using the information of internal sensors. Information obtained by landmark measurement is used to cancel the estimation error which will grow as the travelling distance increases.In this paper we propose a method to select the most suitable landmark among the measurable landmarks from a current position by evaluating the update of the estimation error distribution. We use the extended Kalman filter to update the distribution of estimation error. We show the update characteristics of two types of landmarks, such as line landmarks and point landmarks. For the evaluation of the update we use the width of the distribution ellipse perpendicular to the direction of the path. Through the navigation simulation the effectiveness of the proposed method is confirmed.