A biologically motivated approach towards modular and robust low-level sensor fusion for application in agricultural machinery design

2012 ◽  
Vol 89 ◽  
pp. 10-17 ◽  
Author(s):  
Sebastian Blank ◽  
Tobias Föhst ◽  
Karsten Berns
Keyword(s):  
Sensor Fusion ◽  
Agricultural Machinery ◽  
Low Level ◽  
Level Sensor ◽  
Machinery Design

scholarly journals LiDAR and Camera Fusion Approach for Object Distance Estimation in Self-Driving Vehicles

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 324 ◽  
Author(s):  
G Ajay Kumar ◽  
Jin Hee Lee ◽  
Jongrak Hwang ◽  
Jaehyeong Park ◽  
Sung Hoon Youn ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Distance Estimation ◽  
Autonomous Driving ◽  
Geometric Transformation ◽  
Low Level ◽  
Object Distance ◽  
Level Sensor ◽  
And Performance ◽  
Fusion Approach

The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation, and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the detection of objects at short and long distances. As both the sensors are capable of capturing the different attributes of the environment simultaneously, the integration of those attributes with an efficient fusion approach greatly benefits the reliable and consistent perception of the environment. This paper presents a method to estimate the distance (depth) between a self-driving car and other vehicles, objects, and signboards on its path using the accurate fusion approach. Based on the geometrical transformation and projection, low-level sensor fusion was performed between a camera and LiDAR using a 3D marker. Further, the fusion information is utilized to estimate the distance of objects detected by the RefineDet detector. Finally, the accuracy and performance of the sensor fusion and distance estimation approach were evaluated in terms of quantitative and qualitative analysis by considering real road and simulation environment scenarios. Thus the proposed low-level sensor fusion, based on the computational geometric transformation and projection for object distance estimation proves to be a promising solution for enabling reliable and consistent environment perception ability for autonomous vehicles.

Feature-level sensor fusion

1999 ◽  
Author(s):  
Tamar Peli ◽  
Mon Young ◽  
Robert Knox ◽  
Kenneth K. Ellis ◽  
Frederick Bennett
Keyword(s):  
Sensor Fusion ◽  
Level Sensor

Enabling Knowledge Extraction from Low Level Sensor Data

2011 ◽  
pp. 411-419
Author(s):  
Paolo Cappellari ◽  
Jie Shi ◽  
Mark Roantree ◽  
Crionna Tobin ◽  
Niall Moyna
Keyword(s):  
Knowledge Extraction ◽  
Sensor Data ◽  
Low Level ◽  
Level Sensor
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