Design and comparative evaluation of an iterative contact point estimation method for static stability estimation of mobile actively reconfigurable robots

Due to the advancements of robotic systems, they are able to be employed in more unstructured outdoor environments. In such environments the robot-terrain interaction becomes a highly non-linear function. Several methods were proposed to estimate the robot-terrain interaction: machine learning methods, iterative geometric methods, quasi-static and fully dynamic physics simulations. However, to the best of our knowledge there has been no systematic evaluation comparing those methods. In this paper, we present a newly developed iterative contact point estimation method for static stability estimation of actively reconfigurable robots. This new method is systematically compared to a physics simulation in a comprehensive evaluation. Both interaction models determine the contact points between robot and terrain and facilitate a subsequent static stability prediction. Hence, they can be used in our state space global planner for rough terrain to evaluate the robot's pose and stability. The analysis also compares deterministic versions of both methods to stochastic versions which account for uncertainty in the robot configuration and the terrain model. The results of this analysis show that the new iterative method is a valid and fast approximate method. It is significantly faster compared to a physics simulation while providing good results in realistic robotic scenarios

A Detection System for Dirty Bombs in Open Environments

In this paper, a system is presented, which addresses the first question of source assignment in an open"br" environment of unordered person flows using sophisticated data fusion algorithms for a set of gamma ray"br" detectors and 3D time-of-flight cameras for tracking. The basis of such a system are precise, reliable, and real"br" time updated tracks of all persons within the region of interest. In our system, a set of 3D time-of-flight cameras"br" is used to extract position information of persons in a room."br" In parallel, a set of gamma ray sensors is used to measure the current intensity spatially distributed in the room."br" Nuclear decay is a random process, where the statistics can be described with a Poisson distribution with high"br" variance. Thus, it directly follows that the gamma detection sensors have a poor spatial resolution for allocation"br" of a source. Therefore, the inference of which person is a carrier of radio active material must be based on"br" multiple sensors in order to reduce ambiguities. In the system proposed in this paper, this assignment problem is"br" solved based on Bayesian estimation. At each time step, a prior probability for all possible assignments within"br" the range of a sensor is updated by a likelihood which is modeled as a convolution of a Poisson process and"br" Gaussian distributed measurement noise

Distinctive 3D Surface Entropy Features for Place Recognition

Abstract — In this paper, we present a variant of SURE, an interest point detector and descriptor for 3D point clouds and depth images and use it for recognizing semantically distinct places in indoor environments. The SURE interest operator selects distinctive points on surfaces by measuring the variation in surface orientation based on surface normals in the local vicinity of a point. Furthermore SURE includes a view-poseinvariant descriptor that captures local surface properties and incorporates colored texture information. In experiments, we compare our approach to a state-of-the-art feature detector in depth images (NARF). Finally, we evaluate the use of SURE features for recognizing places and demonstrate its advantages. Index Terms — surface interest points, local shape-texture descriptor, place recognition I

Avoiding to face the challenges of visual place recognition

Through this paper, bottlenecks of conventional place recognition techniques are studied, and a replacement strategy is proposed for each item. Conventional place recognition algorithms are extensions of object recognition techniques applied to larger scale targets known as the place landmarks. The discussion presented in this paper aims to address the challenges of detection and recognition of the places, which make this topic distinctive from detection and recognition of the objects and landmarks. The challenges are listed under related categories. The table of challenges, reasons, and the recommendations to avoid these situations is presented as the guideline for selection of proper tools for place recognition purpose.Accepted versio