Publikationen

Relocalization inside pre-built maps provides a big benefit in the course of today’s autonomous driving tasks where the map can be considered as an additional sensor for refining the estimated current pose of the vehicle. Due to potentially large drifts in the initial pose guess as well as maps containing unfiltered dynamic and temporal static objects (e.g. parking cars), traditional methods like ICP tend to fail and show high computation times. We propose a novel and fast relocalization method for accurate pose estimation inside a pre-built map based on 3D point clouds. The method is robust against inaccurate initialization caused by low performance GPS systems and tolerates the presence of unfiltered objects by specifically learning to extract significant features from current scans and adjacent map sections. More specifically, we introduce a novel distance-based matching loss enabling us to simultaneously extract important information from raw point clouds and aggregating inner- and inter-cloud context by utilizing self- and cross-attention inside a Graph Neural Network. We evaluate StickyLocalization’s (SL) performance through an extensive series of experiments using two benchmark datasets in terms of Relocalization on NuScenes and Loop Closing using KITTI’s Odometry dataset. We found that SL outperforms state-of-the art point cloud registration and relocalization methods in terms of transformation errors and runtime.

Poaceae represent one of the largest plant families in the world. Many species are of great economic importance as food and forage plants while others represent important weeds in agriculture. Although a large number of studies currently address the question of how plants can be best recognized on images, there is a lack of studies evaluating specific approaches for uniform species groups considered difficult to identify because they lack obvious visual characteristics. Poaceae represent an example of such a species group, especially when they are non-flowering. Here we present the results from an experiment to automatically identify Poaceae species based on images depicting six well-defined perspectives. One perspective shows the inflorescence while the others show vegetative parts of the plant such as the collar region with the ligule, adaxial and abaxial side of the leaf and culm nodes. For each species we collected 80 observations, each representing a series of six images taken with a smartphone camera. We extract feature representations from the images using five different convolutional neural networks (CNN) trained on objects from different domains and classify them using four state-of-the art classification algorithms. We combine these perspectives via score level fusion. In order to evaluate the potential of identifying non-flowering Poaceae we separately compared perspective combinations either comprising inflorescences or not. We find that for a fusion of all six perspectives, using the best combination of feature extraction CNN and classifier, an accuracy of 96.1% can be achieved. Without the inflorescence, the overall accuracy is still as high as 90.3%. In all but one case the perspective conveying the most information about the species (excluding inflorescence) is the ligule in frontal view. Our results show that even species considered very difficult to identify can achieve high accuracies in automatic identification as long as images depicting suitable perspectives are available. We suggest that our approach could be transferred to other difficult-to-distinguish species groups in order to identify the most relevant perspectives.