Two kinds of sensory data, namely, camera images and laser ranges, are used as the input to a multilayer forward network to associate the direct transformation ...
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An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360 ° LiDAR, and Ultrasonic Sensors) setup capable of ...
Aug 1, 2020 · The end-to-end deep neural network consists of two parts, which are the multimodal sensor fusion with scene understanding and the driving policy ...
Feb 17, 2024 · This article delves into the fusion of neuromorphic vision and sensor feedback in robotics. It highlights the use of spiking neural networks ...
They designed a simple deep neural network based on ResNet-32 with linear layers to predict the steering angle. The input is a two-channel tensor with an event ...
Oct 21, 2024 · Multi-sensor fusion, at its core, involves integrating data from multiple sensors to make more accurate, reliable, and complete decisions.
The low-level sensor fusion technique is used for direct integration of sensor data, resulting in parameter and state estimates. The multi-layered perceptron, ...
The concept of sensor fusion attempts to replicate the capability of the central nervous system to process sensory inputs from multiple sensors simultaneously.
Sensor fusion is a process of combining sensor data or data derived from disparate sources so that the resulting information has less uncertainty
Sensor fusion: Neural networks can integrate information from multiple sensory modalities to improve perception accuracy. Combining vision, depth, and ...