UAV Active SLAM Trajectory Programming Based on Optimal Control

2013 ◽  
Vol 765-767 ◽  
pp. 1932-1935
Author(s):  
Zeng Xiang Yang ◽  
Sai Jin
Keyword(s):  
Optimal Control ◽  
Optimization Problem ◽  
Simultaneous Localization And Mapping ◽  
Plane Motion ◽  
Programming Algorithm ◽  
Motion Model ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Simulation Results ◽  
Slam Algorithm

To decrease the uncertainty of simultaneous localization and mapping of UAV, and at the same time, to increase the speed of searching the unknown environment at which UAV locates, an active SLAM trajectory programming algorithm is proposed based on optimal control. Therefore, UAV SLAM is tackled as a combined optimization problem, considering the precision of UAV location and mapping integrity. Based on the simplified UAV plane motion model, this algorithm is simulated and tested by comparing with the random SLAM algorithm. Simulation results show that this algorithm is effective.

scholarly journals Simulation and Analysis of Particle Filter Based Slam System

2018 ◽  
Vol 25 (1) ◽  
pp. 137-153
Author(s):  
Piotr Kaniewski ◽  
Paweł Słowak
Keyword(s):  
Particle Filter ◽  
Unmanned Aerial Vehicle ◽  
Simultaneous Localization And Mapping ◽  
Flight Trajectory ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Slam Algorithm

AbstractThe paper describes a problem and an algorithm for simultaneous localization and mapping (SLAM) for an unmanned aerial vehicle (UAV). The algorithm developed by the authors estimates the flight trajectory and builds a map of the terrain below the UAV. As a tool for estimating the UAV position and other parameters of flight, a particle filter was applied. The proposed algorithm was tested and analyzed by simulations and the paper presents a simulator developed by the authors and used for SLAM testing purposes. Chosen simulation results, including maps and UAV trajectories constructed by the SLAM algorithm are included in the paper.

Research on Obstacles Avoidance for UAV SLAM

2014 ◽  
Vol 635-637 ◽  
pp. 1329-1334 ◽  
Author(s):  
Li Wang ◽  
Xu Liu ◽  
Heng Xin Wang ◽  
Xi Bin Wang
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Simultaneous Localization And Mapping ◽  
Plane Motion ◽  
Artificial Potential Field ◽  
Motion Model ◽  
Field Function ◽  
Moving Obstacles ◽  
Localization And Mapping

Abstract: When UAV is implementing the simultaneous localization and mapping (SLAM) problem, the environment where UAV is flying exists unavoidable solid or moving obstacles because of its unknown character, which threatens the flying safety and the completeness of SLAM mission. To conquer this problem, an improved artificial potential field algorithm is proposed to simultaneously accomplish obstacle avoidance of UAV and SLAM mission based on a potential field function containing the distance from UAV to the goal and from UAV to the obstacles and the covariance of features. This algorithm is simulated and tested based on the built UAV plane motion model. The result shows that the proposed algorithm is effective to avoid the obstacles while implementing SLAM for UAV.

Gaussian Kernel Posterior Elimination for Fast Look-Ahead Rao-Blackwellised Particle Filtering for SLAM

2015 ◽  
Vol 781 ◽  
pp. 555-558
Author(s):  
Surasak Nasuriwong ◽  
Peerapol Yuwapoositanon
Keyword(s):  
Particle Filtering ◽  
Simultaneous Localization And Mapping ◽  
Gaussian Kernel ◽  
Fast Computation ◽  
Look Ahead ◽  
Localization And Mapping ◽  
Simulation Results ◽  
Slam Algorithm ◽  
The Look ◽  
Probabilistic Robotics

In this paper, we explore a method for posterior elimination for fast computation of the look-ahead Rao-Blackwellised Particle Filtering (Fast la-RBPF) algorithm for the simultaneous localization and mapping (SLAM) problem in the probabilistic robotics framework. In the case when a lot of SLAM states need to be estimated, large posterior states associated with the correct state may be outnumbered by multiple non-zero smaller posteriors. We show that by masking the low posterior weight states with a Gaussian kernel prior to weight selection the accuracy of the la-RBPF SLAM algorithm can be improved. Simulation results reveal that integrated with the proposed method the fast la-RBPF SLAM performance is enhanced over both the existing RBPF SLAM and the unmodified la-RBPF SLAM algorithms.

scholarly journals An Ant–Based Filtering Random–Finite–Set Approach to Simultaneous Localization and Mapping

2018 ◽  
Vol 28 (3) ◽  
pp. 505-519
Author(s):  
Demeng Li ◽  
Jihong Zhua ◽  
Benlian Xu ◽  
Mingli Lu ◽  
Mingyue Li
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Posterior Density ◽  
Operation System ◽  
Feature Map ◽  
Localization And Mapping ◽  
Finite Set ◽  
Feature Based ◽  
Vehicle Trajectory ◽  
Slam Algorithm ◽  
Random Finite Sets

Abstract Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.

scholarly journals REINFORCEMENT LEARNING HELPS SLAM: LEARNING TO BUILD MAPS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 329-335
Author(s):  
N. Botteghi ◽  
B. Sirmacek ◽  
R. Schulte ◽  
M. Poel ◽  
C. Brune
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
A Priori ◽  
Simultaneous Localization And Mapping ◽  
Indoor Environments ◽  
Robot Localization ◽  
Robust Solution ◽  
Localization And Mapping ◽  
Reward Functions ◽  
Slam Algorithm

Abstract. In this research, we investigate the use of Reinforcement Learning (RL) for an effective and robust solution for exploring unknown and indoor environments and reconstructing their maps. We benefit from a Simultaneous Localization and Mapping (SLAM) algorithm for real-time robot localization and mapping. Three different reward functions are compared and tested in different environments with growing complexity. The performances of the three different RL-based path planners are assessed not only on the training environments, but also on an a priori unseen environment to test the generalization properties of the policies. The results indicate that RL-based planners trained to maximize the coverage of the map are able to consistently explore and construct the maps of different indoor environments.

scholarly journals Visual Simultaneous Localization and Mapping (vSLAM) of Driverless Car in GPS-Denied Areas

2021 ◽  
Vol 12 (1) ◽  
pp. 49
Author(s):  
Abira Kanwal ◽  
Zunaira Anjum ◽  
Wasif Muhammad
Keyword(s):  
Science Fiction ◽  
Urban Areas ◽  
Visual Information ◽  
Dynamic Environment ◽  
Simultaneous Localization And Mapping ◽  
Video Camera ◽  
Input Image ◽  
Localization And Mapping ◽  
Health Related ◽  
Slam Algorithm

A simultaneous localization and mapping (SLAM) algorithm allows a mobile robot or a driverless car to determine its location in an unknown and dynamic environment where it is placed, and simultaneously allows it to build a consistent map of that environment. Driverless cars are becoming an emerging reality from science fiction, but there is still too much required for the development of technological breakthroughs for their control, guidance, safety, and health related issues. One existing problem which is required to be addressed is SLAM of driverless car in GPS denied-areas, i.e., congested urban areas with large buildings where GPS signals are weak as a result of congested infrastructure. Due to poor reception of GPS signals in these areas, there is an immense need to localize and route driverless car using onboard sensory modalities, e.g., LIDAR, RADAR, etc., without being dependent on GPS information for its navigation and control. The driverless car SLAM using LIDAR and RADAR involves costly sensors, which appears to be a limitation of this approach. To overcome these limitations, in this article we propose a visual information-based SLAM (vSLAM) algorithm for GPS-denied areas using a cheap video camera. As a front-end process, features-based monocular visual odometry (VO) on grayscale input image frames is performed. Random Sample Consensus (RANSAC) refinement and global pose estimation is performed as a back-end process. The results obtained from the proposed approach demonstrate 95% accuracy with a maximum mean error of 4.98.

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.

The Study on Mobile Robot Path Planning Based on Frog Leaping Algorithm

2012 ◽  
Vol 490-495 ◽  
pp. 808-812
Author(s):  
Zheng Ran Zhang ◽  
Ji Ying Yin
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Problem ◽  
Optimal Path ◽  
Robot Path Planning ◽  
Unknown Environment ◽  
Simulation Results ◽  
Robot Path

We have proposed a method of robot path planning in a partially unknown environment in this paper. We regard the problem of robot path planning as an optimization problem and solve it with the SFL algorithm. The position of globally best frog in each iterative is selected, and reached by the robot in sequence. The obstacles are detected by the robot sensors are applied to update the information of the environment. The optimal path is generated until the robot reaches its target. The simulation results validate the feasibility of the proposed method.

Using Global Appearance Descriptors to Solve Topological Visual SLAM

2019 ◽  
pp. 1127-1140
Author(s):  
Lorenzo Fernández Rojo ◽  
Luis Paya ◽  
Francisco Amoros ◽  
Oscar Reinoso
Keyword(s):  
Computer Vision ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Simultaneous Localization And Mapping ◽  
Visual Slam ◽  
Sources Of Information ◽  
Topological Approach ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Omnidirectional Images

Mobile robots have extended to many different environments, where they have to move autonomously to fulfill an assigned task. With this aim, it is necessary that the robot builds a model of the environment and estimates its position using this model. These two problems are often faced simultaneously. This process is known as SLAM (simultaneous localization and mapping) and is very common since when a robot begins moving in a previously unknown environment it must start generating a model from the scratch while it estimates its position simultaneously. This chapter is focused on the use of computer vision to solve this problem. The main objective is to develop and test an algorithm to solve the SLAM problem using two sources of information: (1) the global appearance of omnidirectional images captured by a camera mounted on the mobile robot and (2) the robot internal odometry. A hybrid metric-topological approach is proposed to solve the SLAM problem.

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