Key features




Autonomous driving

Factory automation



Faster and data reduced solution for high-resolution LiDAR systems

TimestampsAI is our development for your applications to capture complex scenes in 3D in any environment.

Distance determination method timestampsAI

Our distance determination method TimestampsAI bypasses data-driven problems for all future LiDAR sensor solutions. A pixel-wise quality information given in addition to the resulting point cloud allows confident decision-making of automated systems.

With the compact feature extraction and machine learning algorithms directly based on LiDAR timestamps, future LiDAR systems can be more efficient, effective and robust.

LiDAR data processing - an example LiDAR system with TimestampsAI

LiDAR systems can generate a large amount of data, bringing great challenges to data transmission and processing on a resource-constrained embedded system.  

In factory automation and autonomous driving applications, complex scenes are required to be precisely captured with 3D sensors. Therein, high-resolution LiDAR systems are one promising solution.  

With our services, such as TimestampsAI, you can overcome these issues and get smarter and faster LiDAR solutions for your products. Check out our video and the information on the right to see, what we can do for your LiDAR systems.

Histogram processing vs. TimestampsAI; © Fraunhofer IMS

LiDAR Camera Owl

192 pixel x 2 lines SPAD based camera

Publication of the Fraunhofer IMS

Data Processing Approaches on SPAD-Based d-TOF LiDAR Systems: A Review

Publication of the Fraunhofer IMS

Feature Extraction and Neural Network-based Analysis on Time-correlated LiDAR Histograms

Our technologies - Innovations for your products

Distributed Learning

Distributed learning enables training of complex tasks on multiple small embedded systems.

Hybrid Learning and PGNNs

In case of insufficient training data simulations based on physical models help to improve the data base.

Feature Extraction for Smart Sensors

By means of adapted feature extraction the size of a neural network can be reduced.

Industry 4.0 Research

Within the context of "Industry 4.0", Fraunhofer IMS is researching predictive maintenance solutions for the manufacturing industry.