In recent years, much progress has been made in outdoor autonomous navigation. However, safe navigation is still a daunting challenge in terrain containing vegetation. In this paper, we focus on the segmentation of ladar data into three classes using local three-dimensional point cloud statistics. The classes are: ”scatter ” to represent porous volumes such as grass and tree canopy, ”linear ” to capture thin objects like wires or tree branches, and finally ”surface ” to capture solid objects like ground surface, rocks or large trunks. We present the details of the proposed method, and the modifications we made to implement it on-board an autonomous ground vehicle for real-time data processing. Finally, we present results produced from different stationary laser sensors and from field tests using an unmanned ground vehicle. 1

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Abstract — There is a variety of different terrain types in outdoor environments, each posing different dangers to the robot and demanding a different driving style. In a previous paper, we presented a terrain classification method based on Support Vector Machines (SVM), which uses vibrations induced in the body of the robot to learn different terrain classes. However, in the previous paper, our experimental results were based on vibration data collected by a hand-pulled cart with relatively hard wheels. In this paper, we present experiments on data collected by our RWI ATRV-Jr outdoor robot. Additionally, we compare our SVM-based method to alternative classification methods. The comparison shows that our approach outperforms the other methods. Index Terms — Outdoor robotics, vibration-based terrain classification I.

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Abstract — This paper addresses the problem of vegetation detection from laser measurements. The ability to detect vegetation is important for robots operating outdoors, since it enables a robot to navigate more efficiently and safely in such environments. In this paper, we propose a novel approach for detecting low, grass-like vegetation using laser remission values. In our algorithm, the laser remission is modeled as a function of distance, incidence angle, and material. We classify surface terrain based on 3D scans of the surroundings of the robot. The model is learned in a self-supervised way using vibrationbased terrain classification. In all real world experiments we carried out, our approach yields a classification accuracy of over 99%. We furthermore illustrate how the learned classifier can improve the autonomous navigation capabilities of mobile robots. I.

Laser range scanners have now the ability to acquire millions of 3D points of highly detailed and geometrically complex urban sites, opening new avenues of exploration in modeling urban environments. In the traditional modeling pipeline, range scans are processed off-line after acquisition. The slow sequential acquisition though is a bottleneck. The goal of our work is to alleviate this bottleneck, by exploiting the sequential nature of the data acquisition process. We have developed novel online algorithms, never before used in laser range scanning, that perform data classification on-the-fly as data is being acquired. These algorithms are extremely efficient, and can be potentially integrated with the scanner’s hardware, rendering a sensor that not only acquires but also intelligently processes and classifies the scene points. This sensor, armed with the proposed algorithms, can classify 3D points in real-time as being in vegetation vs. non-vegetation regions, or in horizontal vs. vertical regions. The former classification is possible by the implementation of sequential algorithms through a hidden Markov model (HMM) formulation, and the latter through the use of a combination of cleverly designed sequential detection algorithms. We envision an arsenal of algorithms of this type to be developed in the future. 1.

Abstract — Trajectory planning and optimization is a fundamental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. There are many methods that can generate good movements when given enough time, but planning for high-dimensional robot configuration spaces in realistic environments with many objects in real time remains challenging. This work presents a novel way for faster movement planning in such environments by predicting good path initializations. We build on our previous work on trajectory prediction by adapting it to environments modeled with voxel grids and defining a frame invariant prototype trajectory space. The constructed representations can generalize to a wide range of situations, allowing to predict good movement trajectories and speed up convergence of robot motion planning. An empirical comparison of the effect on planning movements with a combination of different trajectory initializations and local planners is presented and tested on a Schunk arm manipulation platform with laser sensors in simulation and hardware. I.

A common task in bioinformatics is the comparison of biological sequences to probabilistic models in order to evaluate their similarity. Completion of genomes of most of the organisms lead to profitable comparative analyses, providing insights into non-coding regions as well as into protein coding regions of DNA. In the present work we propose a method for finding similar sequence in a database of upstream sequences of DNA. For testing purpose, we have extracted upstream sequences of different mammals of citrate synthase and actin genes and also that of cab gene in different plants. The promoter sequences are extracted from NCBI database. Motifs / TFBS of the upstream sequences are extracted using the software tool ‘TF search’. Then probabilistic models are obtained for motif sequences by HMM method. Query motif sequence can be compared with all the motif sequences in the data base and based on maximum likelihood procedure, degree of similarity between query and all the motif sequences is obtained.

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