Octree map based on sparse point cloud and heuristic probability distribution for labeled images

To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high level contextual understanding of the surroundings is necessary to execute accurate driving maneuvers. This paper presents a novel approach to build three dimensional semantic octree maps from lidar scans and the output of a convolutional neural network (CNN) to obtain the labels of the environment. We present a heuristic method to associate uncertainties to the labels from the images based on a combination of the labels themselves, score maps retrieved by the CNN and the raw images. These uncertainties and the camera-lidar calibration parameters for multiple cameras are considered in the projection of the labels and their uncertainties into the point cloud. Every labeled lidar scan works as an input to an octree map building algorithm that calculates and updates the label probabilities of the voxels in the map. This paper also presents a qualitative and quantitative evaluation of accuracy, analyzing projection in single lidar scans and complete maps built with our probabilistic octree framework.