2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage

Trejos, Kevin; Rincón, Laura; Bolaños, Miguel; Fallas, José; Marín, Leonardo

doi:10.3390/s22186903

Cited by 14 publications

(6 citation statements)

References 43 publications

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“…This enhances maps and reduces accumulated SLAM errors, a process known as loop closure [24]. The wellknown optimization technique called Spare Pose Adjustment (SPA) [23] [38] is utilized in Cartographer SLAM, and a mapscanner is activated whenever a sub-map is generated to close the loop and incorporate that sub-map into the graphic. Two formulas are provided to determine whether a cell is classified as busy, empty, or transitioning to an empty state within a map cell, enhancing comprehension.…”

Section: A Lidar 2d Slam Algorithmsmentioning

confidence: 99%

Efficient Evaluation of SLAM Methods and Integration of Human Detection with YOLO Based on Multiple Optimization in ROS2

Ngoc,

Vinh,

Nguyen

et al. 2023

IJACSA

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In the realm of robotics, indoor robotics is an increasingly prominent field, and enhancing robot performance stands out as a crucial concern. This research undertakes a comparative analysis of various Simultaneous Localization and Mapping (SLAM) algorithms with the overarching objective of augmenting the navigational capabilities of robots. This is accomplished within an open-source framework known as the Robotic Operating System (ROS2) in conjunction with additional software components such as RVIZ and Gazebo. The central aim of this study is to identify the most efficient SLAM approach by evaluating map accuracy and the time it takes for a robot model to reach its destinations when employing three distinct SLAM algorithms: GMapping, Cartographer SLAM, and SLAM_toolbox. Furthermore, this study addresses indoor human detection and tracking assignments, in which we evaluate the effectiveness of YOLOv5, YOLOv6, YOLOv7, and YOLOv8 models in conjunction with various optimization algorithms, including SGD, AdamW, and AMSGrad. The study concludes that YOLOv8 with SGD optimization yields the most favorable outcomes for human detection. These proposed systems are rigorously validated through experimentation, utilizing a simulated Gazebo environment within the Robot Operating System 2 (ROS2).

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Section: A Lidar 2d Slam Algorithmsmentioning

confidence: 99%

Efficient Evaluation of SLAM Methods and Integration of Human Detection with YOLO Based on Multiple Optimization in ROS2

Ngoc,

Vinh,

Nguyen

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In [ 34 ], the authors proposed a localization framework, where a convolutional neural network (CNN) was employed to observe the type and bounding box of the target; another neural network was employed for orientation and distance calculations. Moreover, simultaneous localization and mapping (SLAM) [ 35 ] has received more attention recently; it is capable of constructing maps, positioning, and detecting indoor static objects in real time. Unfortunately, these methods tend to be environmentally vulnerable and LOS conditions are normally required, which may hinder their applications in complex dynamic indoor scenarios.…”

Section: Related Workmentioning

confidence: 99%

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Huang

Guo

et al. 2022

Sensors

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Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least 17.15% smaller average localization errors (ALEs) than existing transfer learning-based positioning methods, which further validates the effectiveness and superiority of our algorithm.

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“…The purpose of this paper is to study and explore a method to improve the Gmapping algorithm and the performance of logistics sorting robots in storage environments, especially in autonomous navigation and obstacle recognition. As a probabilistic SLAM algorithm, the Gmapping algorithm is of great significance to constructing maps and locating robots in unknown environments in real time and has been successfully applied in many fields [16]. This study combines laser sensor data and visual odometry to establish a highresolution grid map to describe the warehouse environment more accurately.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.

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2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage

Cited by 14 publications

References 43 publications

Efficient Evaluation of SLAM Methods and Integration of Human Detection with YOLO Based on Multiple Optimization in ROS2

Efficient Evaluation of SLAM Methods and Integration of Human Detection with YOLO Based on Multiple Optimization in ROS2

Co-Occurrence Fingerprint Data-Based Heterogeneous Transfer Learning Framework for Indoor Positioning

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

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