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3 results for “lifelong mapping”
Data Set htwddKogRob-TSDChangesSim for Localization and Lifelong Mapping
<p>This dataset provides log files recorded in a changed indoor environment with 18 dynamic obstacles. The changes from the original map to the simulated world are highlighted in the figure htwddKogRob-TSDChangesSim_changesHighlighted.png. The total distance travelled in this data set is 179.8 km. The prior knowledge map the robot got to localize and update is shown in htwddKogRob-TSDChangesSim_prior.png and the simulated now changed map is shown in htwddKogRob-TSDChangesSim_groundTruth.png (for both maps: 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m).<br> </p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archives contain ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a simulated environment, the logfiles include both a changed starting position and a ground-truth pose. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Data Set htwddKogRob-TSDReal for Localization and Lifelong Mapping
<p>This dataset represents a 4.7 km long tour (odometry path shown in htwddKogRob-TSDReal_path.png) in an environment whose representation (see map htwddKogRob-TSDReal.png | 1px <span class="math-tex">\(\widehat{=}\)</span> 0.1m) is now obsolete. Several static objects have been moved or removed, and there are varying numbers of dynamic obstacles (people walking around).</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archive contains ascii files, which hold the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfiles hold only the odometry based robot poses. For further information, please refer to the header of the logfiles. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p>
Data Set htwddKogRob-InfReal for Localization and Lifelong Mapping
<p>This small dataset contains real world log files from a 2.2 km long patrol between two points of a previously known map (see htwddKogRob-InfReal.png | 1px <span class="math-tex">\(\widehat{=} \)</span> 0.1m). The environment changes slightly and there are some dynamic obstacles. The figure (see htwddKogRob-InfReal_path.jpg) shows the path driven by the robots according to the real kilometers driven and the patrol points.</p> <p>The work was first presented in:</p> <ul> <li>A Fuzzy-based Adaptive Environment Model for Indoor Robot Localization</li> <li>Authors: Frank Bahrmann, Sven Hellbach, Hans-Joachim Böhme</li> <li>Date of Publication: 2016/10/6</li> <li>Conference: Telehealth and Assistive Technology / 847: Intelligent Systems and Robotics</li> <li>Publisher: ACTA Press</li> </ul> <p>Additionally, we present a video with the proposed algorithm and an insight of this dataset under:</p> <ul> <li>youtube.com/AugustDerSmarte</li> <li>https://www.youtube.com/watch?v=26NBFN_XeQg</li> </ul> <p><strong>Instructions for use</strong></p> <p>The zip archive contains ascii files, which contain the log files of the robot observations and robot poses. Since this data set was recorded in a real environment, the logfile provides only the odometry based robot poses. For further information, please refer to the header of the logfile. To simplify the parsing of the files, you can use these two Java snippets:</p> <p><strong>Laser Range Measurements:</strong></p> <pre><code class="language-java"> List<Double> ranges = new ArrayList<>(numOfLaserRays); List<Error> errors = new ArrayList<>(numOfLaserRays); String s = line.substring(4); String delimiter = "()"; StringTokenizer tokenizer = new StringTokenizer(s, delimiter); while(tokenizer.hasMoreElements()){ String[] arr = tokenizer.nextToken().split(";"); boolean usable = (arr[0].equals("0")?false:true); double range = Double.parseDouble(arr[1]); ranges.add(range); errors.add(usable?Error.OKAY:Error.INVALID_MEASUREMENT); }</code></pre> <p><strong>Poses:</strong></p> <pre><code class="language-java"> String poseString = line.split(":")[2]; String[] elements = poseString.substring(1, poseString.length()-1).split(";"); double x = Double.parseDouble(elements[0]); double y = Double.parseDouble(elements[1]); double phi = Double.parseDouble(elements[2]);</code></pre> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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