HumanPT: Architecture for Low Cost Robotic Applications

Maximum Correntropy Kalman Filter for Orientation Estimation With Application to LiDAR Inertial Odometry

Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions ◽

10.1115/dscc2020-3256 ◽

2020 ◽

Author(s):

Seyed Fakoorian ◽

Matteo Palieri ◽

Angel Santamaria-Navarro ◽

Cataldo Guaragnella ◽

Dan Simon ◽

...

Keyword(s):

Kalman Filtering ◽

Inertial Measurement Unit ◽

Low Cost ◽

Attitude Estimation ◽

High Rate ◽

Measurement Unit ◽

Orientation Estimation ◽

Localization Accuracy ◽

Inertial Measurement ◽

Robotic Applications

Abstract Accurate attitude estimation using low-cost sensors is an important capability to enable many robotic applications. In this paper, we present a method based on the concept of correntropy in Kalman filtering to estimate the 3D orientation of a rigid body using a low-cost inertial measurement unit (IMU). We then leverage the proposed attitude estimation framework to develop a LiDAR-Intertial Odometry (LIO) demonstrating improved localization accuracy with respect to traditional methods. This is of particular importance when the robot undergoes high-rate motions that typically exacerbate the issues associated with low-cost sensors. The proposed orientation estimation approach is first validated using the data coming from a low-cost IMU sensor. We further demonstrate the performance of the proposed LIO solution in a simulated robotic cave exploration scenario.

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Low-Cost Computer Vision Based Real-Time 3D Localization of Object for Robotic Applications

2018 15th IEEE India Council International Conference (INDICON) ◽

10.1109/indicon45594.2018.8987194 ◽

2018 ◽

Author(s):

Vikram Rao ◽

Munesh Singh

Keyword(s):

Computer Vision ◽

Real Time ◽

Low Cost ◽

3D Localization ◽

Robotic Applications

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Implementation of Slam, Navigation, Obstacle Avoidance, and Path Planning of a Robust Mobile Robot Using 2D Laser Scanner

10.32920/ryerson.14643939 ◽

2021 ◽

Author(s):

Omid Karimpour

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Low Cost ◽

Laser Scanner ◽

Memory Consumption ◽

Localization And Mapping ◽

Reliable Performance ◽

Cost Efficient ◽

Office Environments ◽

Robotic Applications

Over the last decade, navigation and Simultaneous Localization and Mapping (SLAM) have become key players in developing robust mobile robots. Several SLAM approaches utilizing camera, laser scan, sonar and fusion of sensors were developed and improved by a number of researchers. In this thesis, comparisons of these methods were evaluated, especially those offering low cost benefits, and low computation and memory consumption. The aim of this thesis was to select the most reliable and cost-efficient approach for indoor autonomous robotic applications. Currently, there are numerous studies that have optimized these SLAM methods; however, they still suffer from various complications such as scale drifting and excessive computation. This study performed different experiments to observe these challenges in realworld environments. A modified Pioneer robot was used to implement the selected SLAM system and furthermore, perform obstacle avoidance and path planning in indoor office environments. The results and tests show the reliable performance of Gmapping after tuning its parameter and set right configurations.

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