An Adaptive Pose Fusion Method for Indoor Map Construction

Zhang, Jinming; Xu, Lianrui; Bao, Cuizhu

doi:10.3390/ijgi10120800

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Autolabor is used as the experimental platform [36]. This robot is equipped with a KINECT V2 camera, an AH-100B inertial measurement unit, and a Rplidar A2 LiDAR (its equipment parameters are shown in Table 1).…”

Section: Experimental Platformmentioning

confidence: 99%

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An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Zhang

Wang

et al. 2022

IJGI

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Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.

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Section: Experimental Platformmentioning

confidence: 99%

“…The three sensors are connected to Autolabor in a solid connection mode (Figure 12). Autolabor is used as the experimental platform [36]. This robot is equipped KINECT V2 camera, an AH-100B inertial measurement unit, and a Rplidar A2 (its equipment parameters are shown in Table 1).…”

Section: Experimental Platformmentioning

confidence: 99%

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Zhang

Wang

et al. 2022

IJGI

Self Cite

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“…In another paper, the authors propose an adaptive pose fusion (APF) method to fuse the robot's pose and use the optimized pose to construct an indoor map [23]. Firstly, the proposed method calculates the robot's pose by the camera and inertial measurement unit (IMU), respectively.…”

Section: Introductionmentioning

confidence: 99%

Processing of Lidar and IMU Data for Target Detection and Odometry of a Mobile Robot

Giannoccaro

Nishida²,

Lay-Ekuakille³

et al. 2023

JAMRIS

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In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is shown, introducing an innovative methodology to manage the data and extracting useful information. The LiDAR sensor is placed on a mobile robot with a modular design that permits the easy change of the number of wheels, which was designed to travel through several environments, saving energy by preliminarily changing the number and arrangement of the wheels in the environment. Furthermore, the robot can recognise landmark in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterise the robot’s pose by surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analysed, highlighting new improvements in the future perspective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.

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Robot Localization Method Based on Multi-Sensor Fusion in Low-Light Environment

Wang,

Lian,

Núñez-Andrés

et al. 2024

Electronics

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When robots perform localization in indoor low-light environments, factors such as weak and uneven lighting can degrade image quality. This degradation results in a reduced number of feature extractions by the visual odometry front end and may even cause tracking loss, thereby impacting the algorithm’s positioning accuracy. To enhance the localization accuracy of mobile robots in indoor low-light environments, this paper proposes a visual inertial odometry method (L-MSCKF) based on the multi-state constraint Kalman filter. Addressing the challenges of low-light conditions, we integrated Inertial Measurement Unit (IMU) data with stereo vision odometry. The algorithm includes an image enhancement module and a gyroscope zero-bias correction mechanism to facilitate feature matching in stereo vision odometry. We conducted tests on the EuRoC dataset and compared our method with other similar algorithms, thereby validating the effectiveness and accuracy of L-MSCKF.

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An Adaptive Pose Fusion Method for Indoor Map Construction

Cited by 4 publications

References 32 publications

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

An Occupancy Information Grid Model for Path Planning of Intelligent Robots

Processing of Lidar and IMU Data for Target Detection and Odometry of a Mobile Robot

Robot Localization Method Based on Multi-Sensor Fusion in Low-Light Environment

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