Sensor fusion for prediction of orientation and position from obstacle using multiple IR sensors an approach based on Kalman filter

2014 ◽  
Author(s):  
K Rahul Sharma ◽  
Daniel Honc ◽  
Frantisek Dusek
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Ir Sensors

MULTI-SENSOR FUSION BASED UAV COLLISION AVOIDANCE SYSTEM

2015 ◽  
Vol 76 (8) ◽  
Author(s):  
Rethnaraj Rambabu ◽  
Muhammad Rijaluddin Bahiki ◽  
Syaril Azrad
Keyword(s):  
Kalman Filter ◽  
Collision Avoidance ◽  
Sensor Fusion ◽  
Obstacle Detection ◽  
Range Data ◽  
Range Estimation ◽  
Flight Tests ◽  
Ir Sensors ◽  
Aerial Vehicle ◽  
Collision Avoidance System

This paper presents the development of a quadrotor unmanned aerial vehicle (UAV) that is capable of detecting and avoiding collision with obstacles through the implementation of Kalman filter-based multi-sensor fusion and cascaded PID position and velocity controllers. Sensor fusion of ultrasonic (US) and infrared (IR) sensors is performed to obtain a reliable range data for obstacle detection which then fed into collision avoidance controller (CAC) for generating necessary response in terms of attitude commands. Results showed that sensor fusion provided accurate range estimation by reducing noises and errors that were present in individual sensors measurements. Flight tests performed proved the capability of UAV to avoid collisions with the obstacle that was introduced to it during flight successfully.

scholarly journals Asynchronous Sensor Fusion using Multi-rate Kalman Filter

2014 ◽  
Vol 63 (11) ◽  
pp. 1551-1558 ◽  
Author(s):  
Young Seop Son ◽  
Wonhee Kim ◽  
Seung-Hi Lee ◽  
Chung Choo Chung
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion

scholarly journals Extended Kalman Filter (EKF) Design for Vehicle Position Tracking Using Reliability Function of Radar and Lidar

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4126 ◽  
Author(s):  
Taeklim Kim ◽  
Tae-Hyoung Park
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Reliability Function ◽  
Position Estimation ◽  
Position Tracking ◽  
Radar Sensors ◽  
Vehicle Position ◽  
Sensor Errors ◽  
Fuzzy Adaptive

Detection and distance measurement using sensors is not always accurate. Sensor fusion makes up for this shortcoming by reducing inaccuracies. This study, therefore, proposes an extended Kalman filter (EKF) that reflects the distance characteristics of lidar and radar sensors. The sensor characteristics of the lidar and radar over distance were analyzed, and a reliability function was designed to extend the Kalman filter to reflect distance characteristics. The accuracy of position estimation was improved by identifying the sensor errors according to distance. Experiments were conducted using real vehicles, and a comparative experiment was done combining sensor fusion using a fuzzy, adaptive measure noise and Kalman filter. Experimental results showed that the study’s method produced accurate distance estimations.

Sign in / Sign up

Export Citation Format

Share Document