Simultaneous Localization and Mapping (SLAM)

Artificial intelligence methods (esp. deep learning) have made great progress over classical methods in the field of camera and LiDAR data processing. The major advantage is that the neural networks used can be trained with only a set of input data (e.g., images) and the desired output data (e.g., motion estimation) without having to manually design special algorithms for the problem. Through this so-called “end-to-end” training, the internal network parameters are learned automatically so that an optimal result is obtained.

The figure below shows the architecture of a deep neural network that estimates camera motion (right “pose”) from a sequence of images (left) [1]. Here, it consists of a convolutional network (CNN, yellow) that estimates relative motion from two consecutive images, and a recurrent network (RNN) that learns the motion dynamics and infers the relative motion (pose). RNNs can process sequences (e.g., speech, text) and can learn the timing of the input data through their internal “memory.” The figure below shows the estimated trajectory of a car trip (in red).

Camera, as well as LiDAR data, are used for positioning as well as 3D map generation. Furthermore, these data can be used to create digital HD maps. These contain annotations of static objects, which are georeferenced. These digital maps can also be used to support the position solution.