516 research outputs found
Max Lilienthal papers 1846-1847
No full textContains one Hanukkah sermon (final page missing) translated from German by Samuel Lilienthal, M.D., brother of the author, for Cong. Bnai Israel in Augusta, Ga. The manuscript contains corrections and printer's notations in a second handThe sermon was published in The Occident, vol. 4, no. 12 (Mar., 1847). Also contains 1 page of minutes from 1847 meeting of NY Beth DinGift of Yosef Hayim Yerushalm
Sketch maps dataset
No full textA dataset of 25 sketch-maps obtained from an [interface](aass.oru.se/Research/mro/smokebot/sketchmap-web/) in a web browser. The sketch correspond to the ground truth of KTH dataset for SLAM.
Each sketch is associated with two possible ground truth segmentatino given by two independent users.
If you use this dataset please cite this paper:
@Article{robotics8020043,
AUTHOR = {Mielle, Malcolm and Magnusson, Martin and Lilienthal, Achim J.},
TITLE = {URSIM: Unique Regions for Sketch Map Interpretation and Matching},
JOURNAL = {Robotics},
VOLUME = {8},
YEAR = {2019},
NUMBER = {2},
ARTICLE-NUMBER = {43},
URL = {https://www.mdpi.com/2218-6581/8/2/43},
ISSN = {2218-6581},
ABSTRACT = {We present a method for matching sketch maps to a corresponding metric map, with the aim of later using the sketch as an intuitive interface for human–robot interactions. While sketch maps are not metrically accurate and many details, which are deemed unnecessary, are omitted, they represent the topology of the environment well and are typically accurate at key locations. Thus, for sketch map interpretation and matching, one cannot only rely on metric information. Our matching method first finds the most distinguishable, or unique, regions of two maps. The topology of the maps, the positions of the unique regions, and the size of all regions are used to build region descriptors. Finally, a sequential graph matching algorithm uses the region descriptors to find correspondences between regions of the sketch and metric maps. Our method obtained higher accuracy than both a state-of-the-art matching method for inaccurate map matching, and our previous work on the subject. The state of the art was unable to match sketch maps while our method performed only 10% worse than a human expert.},
DOI = {10.3390/robotics8020043}
Improved gas source localization with a mobile robot by learning analytical gas dispersal models from statistical gas distribution maps using evolutionary algorithms
No full textThe method presented in this chapter computes an estimate of the location of a single gas sourcefrom a set of localised gas sensor measurements. The estimation process consists of three steps.First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm isapplied, which carries out spatial integration by convolving localised sensor readings and modelling theinformation content of the point measurements with a Gaussian kernel. The statistical gas distributiongrid map averages out the transitory effects of turbulence and converges to a representation of thetime-averaged spatial distribution of a target gas. The second step is to learn the parameters ofan analytical model of average gas distribution. Learning is achieved by nonlinear least squaresfitting of the analytical model to the statistical gas distribution map using Evolution Strategies (ES),which are a special type of Evolutionary Algorithms (EA). This step provides an analysis of thestatistical gas distribution map regarding the airflow conditions and an alternative estimate of thegas source location, i.e. the location predicted by the analytical model in addition to the location ofthe maximum in the statistical gas distribution map. In the third step, an improved estimate of thegas source position can then be derived by considering the maximum in the statistical gas distributionmap, the best fit as well as the corresponding fitness value. Different methods to select the mosttruthful estimate are introduced and a comparison regarding their accuracy is presented, based on atotal of 34 hours of gas distribution mapping experiments with a mobile robot. This chapter is anextended version of a paper by the authors (Lilienthal et al. [2005])
The Next Step in Robot Commissioning : Autonomous Picking and Palletizing
No full textSo far, autonomous order picking (commissioning) systems have not been able to meet the stringent demands regarding speed, safety, and accuracy of real-world warehouse automation, resulting in reliance on human workers. In this letter, we target the next step in autonomous robot commissioning: automatizing the currently manual order picking procedure. To this end, we investigate the use case of autonomous picking and palletizing with a dedicated research platform and discuss lessons learned during testing in simplified warehouse settings. The main theoretical contribution is a novel grasp representation scheme which allows for redundancy in the gripper pose placement. This redundancy is exploited by a local, prioritized kinematic controller which generates reactive manipulator motions on-the-fly. We validated our grasping approach by means of a large set of experiments, which yielded an average grasp acquisition time of 23.5 s at a success rate of 94.7%. Our system is able to autonomously carry out simple order picking tasks in a humansafe manner, and as such serves as an initial step toward future commercial-scale in-house logistics automation solutions
Exploration and localization of a gas source with MOX gas sensors on a mobile robot-A Gaussian regression bout amplitude approach
No full textMobile robot olfaction systems combine gas sensors with mobility provided by robots. They relief humans of dull, dirty and dangerous tasks in applications such as search & rescue or environmental monitoring. We address gas source localization and especially the problem of minimizing exploration time of the robot, which is a key issue due to energy constraints. We propose an active search approach for robots equipped with MOX gas sensors and an anemometer, given an occupancy map. Events of rapid change in the MOX sensor signal ('bouts') are used to estimate the distance to a gas source. The wind direction guides a Gaussian regression, which interpolates distance estimates. The contributions of this paper are two-fold. First, we extend previous work on gas source distance estimation with MOX sensors and propose a modification to cope better with turbulent conditions. Second, we introduce a novel active search gas source localization algorithm and validate it in a real-world environment
Robust Frequency-Based Structure Extraction
No full textState of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.ILIA
Improved Gas Source Localization with a Mobile Robot by Learning Analytical Gas Dispersal Models from Statistical Gas Distribution Maps Using Evolutionary Algorithms
No full textThe method presented in this chapter computes an estimate of the location of a single gas source from a set of localized gas sensor measurements. The estimation process consists of three steps. First, a statistical model of the time-averaged gas distribution is estimated in the form of a two-dimensional grid map. In order to compute the gas distribution grid map the Kernel DM algorithm is applied, which carries out spatial integration by convolving localized sensor readings and modeling the information content of the point measurements with a Gaussian kernel. The statistical gas distribution grid map averages out the transitory effects of turbulence and converges to a representation of the time-averaged spatial distribution of a target gas. The second step is to learn the parameters of an analytical model of average gas distribution. Learning is achieved by nonlinear least squares fitting of the analytical model to the statistical gas distribution map using Evolution Strategies (ES), which are a special type of Evolutionary Algorithm (EA). This step provides an analysis of the statistical gas distribution map regarding the airflow conditions and an alternative estimate of the gas source location, i.e. the location predicted by the analytical model in addition to the location of the maximum in the statistical gas distribution map. In the third step, an improved estimate of the gas source position can then be derived by considering the maximum in the statistical gas distribution map, the best fit, as well as the corresponding fitness value. Different methods to select the most truthful estimate are introduced, and a comparison regarding their accuracy is presented, based on a total of 34 hours of gas distribution mapping experiments with a mobile robot. This chapter is an extended version of the conference paper (Lilienthal et al., 2005).</jats:p
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