Multimodal Data Generation
Here, we adopted the Delaunay Triangulation (DT) as a technique to obtain high-resolution maps. DT generates a mesh from the projected sparse depth points on the camera coordinate system. The nearest neighbors were used to interpolate the unsampled locations of the map. The dense maps are obtained solely from LIDAR data thus, data (color or texture) from the camera is not used in the maps. Besides the depth map (DM), a dense reﬂectance map (RM) is also considered in the vehicle detection system. In the case of DM, the variable to be interpolated is the range (distance), while the reﬂectance value (reﬂection return) is the variable to be interpolated to generate the RM. The reﬂectivity attribute is related to the type of surface the LIDAR reﬂection is obtained.
The algorithm is described in:
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto, U. Nunes, “Multimodal Vehicle Detection: Fusing 3D-LIDAR and Color Camera Data,” Pattern Recognition Letters, Elsevier, 2017. (In Press) DOI: 10.1016/j.patrec.2017.09.038
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto, and U. Nunes, “DepthCN: Vehicle Detection Using 3D-LIDAR and ConvNet,” In Proceedings of IEEE 20th International Conference on Intelligent Transportation Systems (ITSC 2017), Yokohama, Japan. DOI: 10.1109/ITSC.2017.8317880
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto, U. Nunes, “Real-Time Deep ConvNet-based Vehicle Detection Using 3D-LIDAR Reflection Intensity Data,” Robot 2017: Third Iberian Robotics Conference. Springer, 2017. (Book Chapter) DOI: 10.1007/978-3-319-70836-2_39
An example of vehicle detection using the reflection intensity is described in the below. Considering a 3D-LIDAR mounted on board a robotic vehicle, which is calibrated with respect to a monocular camera, a Dense Reflection Map (DRM) is generated from the projected sparse LIDAR’s reflectance intensity, and inputted to a Deep Convolutional Neural Network (ConvNet) object detection framework (YOLOv2) for the vehicle detection. Watch the result in the video below.