scholarly journals A high-efficiency, information-based exploration path planning method for active simultaneous localization and mapping

2020 ◽  
Vol 17 (1) ◽  
pp. 172988142090320
Author(s):  
Peng Li ◽  
Cai-yun Yang ◽  
Rui Wang ◽  
Shuo Wang
Keyword(s):  
Path Planning ◽  
Information Entropy ◽  
High Efficiency ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Coverage Area ◽  
High Coverage ◽  
Full Coverage ◽  
Localization And Mapping

The efficiency of exploration in an unknown scene and full coverage of the scene are essential for a robot to complete simultaneous localization and mapping actively. However, it is challenging for a robot to explore an unknown environment with high efficiency and full coverage autonomously. In this article, we propose a novel exploration path planning method based on information entropy. An information entropy map is first constructed, and its boundary features are extracted. Then a Dijkstra-based algorithm is applied to generate candidate exploration paths based on the boundary features. The dead-reckoning algorithm is used to predict the uncertainty of the robot’s pose along each candidate path. The exploration path is selected based on exploration efficiency and/or high coverage. Simulations and experiments are conducted to evaluate the proposed method’s effectiveness. The results demonstrated that the proposed method achieved not only higher exploration efficiency but also a larger coverage area.

scholarly journals Autonomous navigation in unknown environment using sliding mode SLAM and genetic algorithm

2021 ◽  
Author(s):  
Salvador Ortiz ◽  
Wen Yu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Sliding Mode Control ◽  
Autonomous Navigation ◽  
Sliding Mode ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Bounded Uncertainties

In this paper, sliding mode control is combined with the classical simultaneous localization and mapping (SLAM) method. This combination can overcome the problem of bounded uncertainties in SLAM. With the help of genetic algorithm, our novel path planning method shows many advantages compared with other popular methods.

scholarly journals Path Planning for Autonomous Vehicles Using QZSS and Satellite Visibility Map

2013 ◽  
Vol 25 (2) ◽  
pp. 400-407 ◽  
Author(s):  
Mitsunori Kitamura ◽  
◽  
Yoichi Yasuoka ◽  
Taro Suzuki ◽  
Yoshiharu Amano ◽  
...  
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Dead Reckoning ◽  
Path Following ◽  
Planning Method ◽  
Self Control ◽  
Potential Fields ◽  
Visibility Map ◽  
Gps Satellites ◽  
Satellite Visibility

This paper describes a path planning method that uses the Quasi-Zenith Satellites System(QZSS) and a satellite visibility map for autonomous vehicles. QZSS is a positioning system operated by Japan that has an effect similar to an increase in the number of GPS satellites. Therefore, QZSS can be used to improve the availability of GPS positioning. A satellite visibility map is a special map that simulates the number of visible satellites at all points on the map. The vehicle can use the satellite visibility map to determine the points that receive more satellite signals. The proposed method generates the artificial potential fields from the satellite visibility map and obstacle information around the vehicle, and it generates the path following the potential fields. Thereby, the vehicle can select the path that has more satellite signals, improving the availability of GPS fixed solutions. Hence, the vehicle can reduce the accumulated error by dead reckoning, and it can improve the safety of self-control. In this study, we evaluate the satellite visibility maps and the path planning method. The results show that the proposed method does improve the availability of GPS fixed solutions.

scholarly journals A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 5 ◽  
Author(s):  
Guangchao Hou ◽  
Qi Shao ◽  
Bo Zou ◽  
Liwen Dai ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Online Algorithm ◽  
Low Cost ◽  
Autonomous Underwater Vehicles ◽  
Low Frequency ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Low Complexity ◽  
Petroleum Engineering ◽  
Long Baseline ◽  
Localization And Mapping

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

scholarly journals Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Mu Zhou ◽  
Kunjie Xu ◽  
Zengshan Tian ◽  
Haibo Wu ◽  
Ruikang Shi
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Location Based Services ◽  
Alignment Algorithm ◽  
Location Tracking ◽  
Crowd Sourcing ◽  
Coverage Area ◽  
Localization And Mapping ◽  
Vision Sensors ◽  
Clock Location ◽  
Intensive Work

Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.

scholarly journals Automatic Identification System (AIS) Dynamic Data Integrity Monitoring and Trajectory Tracking Based on the Simultaneous Localization and Mapping (SLAM) Process Model

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8430
Author(s):  
Krzysztof Jaskólski ◽  
Łukasz Marchel ◽  
Andrzej Felski ◽  
Marcin Jaskólski ◽  
Mariusz Specht
Keyword(s):  
Process Model ◽  
Dead Reckoning ◽  
Simultaneous Localization And Mapping ◽  
Process Models ◽  
Automatic Identification ◽  
Identification System ◽  
Dynamic Data ◽  
Integrity Monitoring ◽  
Automatic Identification System ◽  
Localization And Mapping

To enhance the safety of marine navigation, one needs to consider the involvement of the automatic identification system (AIS), an existing system designed for ship-to-ship and ship-to-shore communication. Previous research on the quality of AIS parameters revealed problems that the system experiences with sensor data exchange. In coastal areas, littoral AIS does not meet the expectations of operational continuity and system availability, and there are areas not covered by the system. Therefore, in this study, process models were designed to simulate the tracking of vessel trajectories, enabling system failure detection based on integrity monitoring. Three methods for system integrity monitoring, through hypotheses testing with regard to differences between model output and actual simulated vessel positions, were implemented, i.e., a Global Positioning System (GPS) ship position model, Dead Reckoning and RADAR Extended Kalman Filter (EKF)—Simultaneous localization and mapping (SLAM) based on distance and bearing to navigational aid. The designed process models were validated on simulated AIS dynamic data, i.e., in a simulated experiment in the area of Gdańsk Bay. The integrity of AIS information was determined using stochastic methods based on Markov chains. The research outcomes confirmed the usefulness of the proposed methods. The results of the research prove the high level (~99%) of integrity of the dynamic information of the AIS system for Dead Reckoning and the GPS process model, while the level of accuracy and integrity of the position varied depending on the distance to the navigation aid for the RADAR EKF-SLAM process model.

scholarly journals Distributed and collaborative monocular simultaneous localization and mapping for multi-robot systems in large-scale environments

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878017 ◽  
Author(s):  
Hui Zhang ◽  
Xieyuanli Chen ◽  
Huimin Lu ◽  
Junhao Xiao
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Simultaneous Localization And Mapping ◽  
Small Scale ◽  
Data Set ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Mapping System ◽  
Robot Systems ◽  
Multi Robot

In this article, we propose a distributed and collaborative monocular simultaneous localization and mapping system for the multi-robot system in large-scale environments, where monocular vision is the only exteroceptive sensor. Each robot estimates its pose and reconstructs the environment simultaneously using the same monocular simultaneous localization and mapping algorithm. Meanwhile, they share the results of their incremental maps by streaming keyframes through the robot operating system messages and the wireless network. Subsequently, each robot in the group can obtain the global map with high efficiency. To build the collaborative simultaneous localization and mapping architecture, two novel approaches are proposed. One is a robust relocalization method based on active loop closure, and the other is a vision-based multi-robot relative pose estimating and map merging method. The former is used to solve the problem of tracking failures when robots carry out long-term monocular simultaneous localization and mapping in large-scale environments, while the latter uses the appearance-based place recognition method to determine multi-robot relative poses and build the large-scale global map by merging each robot’s local map. Both KITTI data set and our own data set acquired by a handheld camera are used to evaluate the proposed system. Experimental results show that the proposed distributed multi-robot collaborative monocular simultaneous localization and mapping system can be used in both indoor small-scale and outdoor large-scale environments.

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