Research on Mobile Robot SLAM in Dynamic Environment

2011 ◽  
Vol 130-134 ◽  
pp. 232-238
Author(s):  
Bai Fan Chen ◽  
Zi Xing Cai
Keyword(s):  
Mobile Robot ◽  
Experimental Test ◽  
Data Association ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Target Model ◽  
Dynamic Objects ◽  
Objects Detection

A SLAMiDE（SLAM in Dynamic Environments) system is designed and realized in the paper, which supplies a holistic framework and a series of implementation methods for mobile robot SLAM in dynamic environments. A uniform target model is proposed in SLAMiDE system. The dynamic targets and static targets and the mobile robot pose are estimated simultaneously, by synthesized the research of the data association and dynamic targets detection and static SLAM based on local maps. Finally, the results of the experimental test prove that the SLAMiDE system can realize dynamic objects detection and mapping and location correctly.

scholarly journals A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5889
Author(s):  
Yu Zhang ◽  
Xiping Xu ◽  
Ning Zhang ◽  
Yaowen Lv
Keyword(s):  
Dynamic Environment ◽  
Dynamic Environments ◽  
Data Sets ◽  
Panoramic Image ◽  
Feature Points ◽  
Moving Targets ◽  
Panoramic Cameras ◽  
Public Data ◽  
Dynamic Objects ◽  
Better Than

When a traditional visual SLAM system works in a dynamic environment, it will be disturbed by dynamic objects and perform poorly. In order to overcome the interference of dynamic objects, we propose a semantic SLAM system for catadioptric panoramic cameras in dynamic environments. A real-time instance segmentation network is used to detect potential moving targets in the panoramic image. In order to find the real dynamic targets, potential moving targets are verified according to the sphere’s epipolar constraints. Then, when extracting feature points, the dynamic objects in the panoramic image are masked. Only static feature points are used to estimate the pose of the panoramic camera, so as to improve the accuracy of pose estimation. In order to verify the performance of our system, experiments were conducted on public data sets. The experiments showed that in a highly dynamic environment, the accuracy of our system is significantly better than traditional algorithms. By calculating the RMSE of the absolute trajectory error, we found that our system performed up to 96.3% better than traditional SLAM. Our catadioptric panoramic camera semantic SLAM system has higher accuracy and robustness in complex dynamic environments.

scholarly journals Cash Collection Model of Electric Power Business Office Based on Computer Algorithm

2022 ◽  
Vol 2146 (1) ◽  
pp. 012023
Author(s):  
Binghua Guo ◽  
Nan Guo
Keyword(s):  
Mobile Robot ◽  
Medical Treatment ◽  
Electric Power ◽  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Main Trend ◽  
Intelligent Robot ◽  
Treatment Service ◽  
Intelligent Algorithms

Abstract With the continuous development of intelligent algorithms, mobile robot (hereinafter referred to as MR) technology is gradually mature, which has been widely used in a variety of industries, such as industry, agriculture, medical treatment, service and so on. With the improvement of intelligent level, people have higher and higher requirements for MRs, which requires MRs to constantly adapt to different environments, especially dynamic environments. In the dynamic environment, obstacle avoidance technology has become the focus of intelligent robot research, which needs to continuously develop a variety of algorithms. By combining a variety of algorithms, we can realize obstacle avoidance and PP (hereinafter referred to as PP) of MR, which can realize obstacle avoidance more efficiently, in real time and intelligently. Multi algorithm fusion of MR has become the main trend of obstacle avoidance in the future, which will realize PP and optimization. Firstly, this paper analyzes the differences between traditional algorithms and intelligent algorithms. Then, the kinematics model and PP algorithm of MR are analyzed. Finally, the simulation is carried out.

scholarly journals Robust Stereo Visual Inertial Navigation System Based on Multi-Stage Outlier Removal in Dynamic Environments

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2922 ◽  
Author(s):  
Dinh Van Nam ◽  
Kim Gon-Woo
Keyword(s):  
Mobile Robot ◽  
State Estimation ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Dynamic Environments ◽  
Indoor Environments ◽  
Outlier Removal ◽  
Multi Stage ◽  
Dynamic Objects

Robotic mapping and odometry are the primary competencies of a navigation system for an autonomous mobile robot. However, the state estimation of the robot typically mixes with a drift over time, and its accuracy is degraded critically when using only proprioceptive sensors in indoor environments. Besides, the accuracy of an ego-motion estimated state is severely diminished in dynamic environments because of the influences of both the dynamic objects and light reflection. To this end, the multi-sensor fusion technique is employed to bound the navigation error by adopting the complementary nature of the Inertial Measurement Unit (IMU) and the bearing information of the camera. In this paper, we propose a robust tightly-coupled Visual-Inertial Navigation System (VINS) based on multi-stage outlier removal using the Multi-State Constraint Kalman Filter (MSCKF) framework. First, an efficient and lightweight VINS algorithm is developed for the robust state estimation of a mobile robot by practicing a stereo camera and an IMU towards dynamic indoor environments. Furthermore, we propose strategies to deal with the impacts of dynamic objects by using multi-stage outlier removal based on the feedback information of estimated states. The proposed VINS is implemented and validated through public datasets. In addition, we develop a sensor system and evaluate the VINS algorithm in the dynamic indoor environment with different scenarios. The experimental results show better performance in terms of robustness and accuracy with low computation complexity as compared to state-of-the-art approaches.

scholarly journals Motion Planning and Control of an Omnidirectional Mobile Robot in Dynamic Environments

Robotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 48
Author(s):  
Mahmood Reza Azizi ◽  
Alireza Rastegarpanah ◽  
Rustam Stolkin
Keyword(s):  
Mobile Robot ◽  
Collision Avoidance ◽  
Motion Control ◽  
Equations Of Motion ◽  
Dynamic Environments ◽  
Discrete State ◽  
Omnidirectional Mobile Robot ◽  
Planning And Control ◽  
Avoidance Strategy ◽  
And Performance

Motion control in dynamic environments is one of the most important problems in using mobile robots in collaboration with humans and other robots. In this paper, the motion control of a four-Mecanum-wheeled omnidirectional mobile robot (OMR) in dynamic environments is studied. The robot’s differential equations of motion are extracted using Kane’s method and converted to discrete state space form. A nonlinear model predictive control (NMPC) strategy is designed based on the derived mathematical model to stabilize the robot in desired positions and orientations. As a main contribution of this work, the velocity obstacles (VO) approach is reformulated to be introduced in the NMPC system to avoid the robot from collision with moving and fixed obstacles online. Considering the robot’s physical restrictions, the parameters and functions used in the designed control system and collision avoidance strategy are determined through stability and performance analysis and some criteria are established for calculating the best values of these parameters. The effectiveness of the proposed controller and collision avoidance strategy is evaluated through a series of computer simulations. The simulation results show that the proposed strategy is efficient in stabilizing the robot in the desired configuration and in avoiding collision with obstacles, even in narrow spaces and with complicated arrangements of obstacles.

scholarly journals ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

2021 ◽  
Author(s):  
Jiahui Huang ◽  
Sheng Yang ◽  
Zishuo Zhao ◽  
Yu-Kun Lai ◽  
Shi-Min Hu
Keyword(s):  
Motion Estimation ◽  
Rigid Body ◽  
Iterative Scheme ◽  
Dynamic Environments ◽  
Rigid Bodies ◽  
Factor Graph ◽  
Agglomerative Clustering ◽  
Multiple Objects ◽  
Graph Optimization ◽  
Dynamic Objects

AbstractWe present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

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