Alexandros Makris

Computing occupancy grids from multiple sensors using linear opinion pools

Perception is a key component for any robotic system. In this paper we present a method to construct occupancy grids by fusing sensory information using Linear Opinion Pools. We used lidar sensors and a stereo-vision system mounted on a vehicle to make the experiments. To perform the validation, we compared the proposed method with the fusion method previously used in the Bayesian Occupancy Filter framework, using real data taken from highway and urban scenarios. The results show that our method is better at dealing with conflicting information coming from the sensors. We propose an implementation on parallel hardware which allows real-time execution.

54 Awareness of Road Scene Participants for Autonomous Driving

This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model-based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid-based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.

Probabilistic Integration of Intensity and Depth Information for Part-Based Vehicle Detection

In this paper, an object class recognition method is presented. The method uses local image features and follows the part-based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weigh the probability of finding the object at a given distance. To train the system for an object class, only a database of images annotated with bounding boxes is required, thus automatizing the extension of the system to different object classes. We apply our method to the problem of detecting vehicles from a moving platform. The experiments with a data set of stereo images in an urban environment show a significant improvement in performance when using both information modalities.

Bayesian Multiple-Hypothesis Tracking of Merging and Splitting Targets

This paper presents a Bayesian model for the multiple-target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and target states. The events include target births, deaths, splits, and merges. The target states contain both the target positions and attributes. By updating the target attributes and conditioning the events on their updated values, we can include high-level domain knowledge into the system. This strategy improves the tracking accuracy and the computational efficiency since we focus only on likely events for each situation. A two-step multiple-hypothesis tracking algorithm has been developed to estimate the model state. The proposed approach is tested by both simulation and real data for mesoscale convective system tracking.

Stochastic tracking of mesoscale convective systems: evaluation in the West African Sahel

In this work we apply a recently proposed Bayesian multiple target tracking model to mesoscale convective systems tracking. This stochastic model follows the multiple hypothesis tracking paradigm and can handle a varying number of targets while detecting the target birth, death, split, and merge events. The model is tested experimentally with real MCS targets detected from meteosat IR data over the Sahelian region. The performance of the stochastic tracking is evaluated by comparing it qualitatively and quantitatively with well established deterministic methods.

Data assimilation with state alignment using high-level image structures detection

Sequential and variational assimilation methods allow tracking physical states using dynamic prior together with external observation of the studied system. However, when dense image satellite observations are available, such approaches realize a correction of the amplitude of the different state values but do not incorporate the spatial errors of structure positions. In the case of the position of a vortex, for example, when there is misfit between state and observation, the processes can be long to converge and even diverge when high dimensional state spaces are treated with few iterations of the assimilation methods as it is the case in operational algorithms. In this paper, we tackle this issue by proposing an alignment method based on modern object detection methods that uses visual correspondences between the physical state model and the structural information given by a sequence of image observing the phenomena.