Perception Technologies

AMPL experience in perception technologies have provided state of the art solutions.

Deep Learning for perception applications

Current technologies take advantage on the availability of powerful GPUs which allows the creation of state of the art Neural Networks which allows to detect and identify patterns in images and point clouds.
​
Our lab has wide experience in developing Deep Learning techniques that has been used in industry and academia for a wide diversity of applications: Autonomous vehicles perception, quality control, drones technologies are some of the examples where our technology has been successfully tested and implemented.

Our 3D detection software stack is composed of different deep neural networks, which is able to process the information gathered by the most popular sensors used in robotics applications: cameras and LiDARs. 

Image detection

Image re-identification

3D Detections

Obtaining 3D measurements can be performed in different ways: using a camera and a laser, using two or more cameras and more recently, using 3D cameras also provide color information.

AMPL has experience using 3D technology, it has been performed real time large objects, calibration and synchronization of multiple cameras and the use of RGB-D cameras for modeling environments.

Sensor Fusion

3D Reconstruction

The 3D and 2D information is treated in combination with other sources or just in form of point cloud. The lab has proven it usability in different application, such as surveillance,  automotive or inspection. Some examples of 3D applications developed by AMPL are:

LiDAR Fusion

Vision Applied to Robots

Sensor Setup

Apart from our software portfolio, AMPL know-how includes wide experience in sensor configuration to select the optimal setup of devices for each specific problem. Moreover, we provide state-of-the-art calibration and synchronization technologies for multi-sensor systems.

Synchronization

Localization

Another technology that AMPL has successfully developed is Fusing our lidar localization solution with GNSS information; where GNSS data is used to improve localization accuracy in places with less map features and landmarks and to prevent the kidnapped robot problem.
Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, or error, allowing AMPLS’s data fusion localization to be used in specifically difficult scenarios for GNSS such as urban. 

Our Approach