@inproceedings{SpiessFriesslichBluemmetal., author = {Spieß, Florian and Friesslich, Jonas and Bluemm, Daniel and Mast, Fabio and Vinokour, Dmitrij and Kounev, Samuel and Kaupp, Tobias and Strobel, Norbert}, title = {Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment}, series = {2021 20th International Conference on Advanced Robotics (ICAR)}, booktitle = {2021 20th International Conference on Advanced Robotics (ICAR)}, doi = {10.1109/ICAR53236.2021.9659355}, pages = {857 -- 864}, abstract = {To arrive at a realistic assessment of localization methods in terms of their performance in an industrial environment under various challenging conditions, we provide a benchmark to evaluate algorithms both for individual components as well as multi-sensor systems. For several sensor types, including wheel odometry, RGB cameras, RGB-D cameras, and LIDAR, potential issues were identified. The accuracy of wheel odometry, for example, when there are bumps on the track. For each sensor type, we explicitly chose a track for the benchmark dataset containing situations where the sensor fails to provide adequate measurements. Based on the acquired sensor data, localization can be achieved either using a single sensor information or sensor fusion. To help evaluate the output of associated localization algorithms, we provide a software to evaluate a set of metrics as part of the paper. An example application of the benchmark with state-of-the-art algorithms for each sensor is also provided.}, language = {en} } @article{SpiessFriesslichKauppetal., author = {Spiess, Florian and Friesslich, Jonas and Kaupp, Tobias and Kounev, Samuel and Strobel, Norbert}, title = {Survey and Experimental Comparison of RGB-D Indoor Robot Navigation Methods Supported by ROS and Their Expansion via Fusion with Wheel Odometry and IMU Data}, series = {International Journal of Mechanical Engineering and Robotics Research}, volume = {9}, journal = {International Journal of Mechanical Engineering and Robotics Research}, number = {12}, doi = {10.18178/ijmerr.9.12.1532-1540}, pages = {1532 -- 1540}, language = {en} } @article{SpiessReinhartStrobeletal., author = {Spiess, Florian and Reinhart, Lukas and Strobel, Norbert and Kaupp, Tobias and Kaiser, Dennis and Kounev, Samuel}, title = {People detection with depth silhouettes and convolutional neural networks on a mobile robot}, series = {Journal of Image and Graphics}, volume = {9}, journal = {Journal of Image and Graphics}, number = {4}, pages = {135 -- 139}, abstract = {This paper presents a novel people detection approach for mobile robot applications based on a combination of classical computer vision techniques and a state-of-the-art neural network. Our approach involves an RGB-D camera as an environmental sensor. The depth data is used to extract silhouettes around people. The RGB images are subsequently augmented with this border information before passing it to the neural network. Under challenging lighting conditions, our system was able to outperform the neural network trained on regular RGB data alone by a factor of two.}, language = {en} } @inproceedings{SpiessStrobelKauppetal., author = {Spiess, Florian and Strobel, Norbert and Kaupp, Tobias and Kounev, Samuel}, title = {A data-driven Sensor Model for LIDAR Range Measurements used for Mobile Robot Navigation}, series = {2022 Sixth IEEE International Conference on Robotic Computing (IRC)}, booktitle = {2022 Sixth IEEE International Conference on Robotic Computing (IRC)}, doi = {10.1109/IRC55401.2022.00020}, pages = {76 -- 80}, abstract = {In this paper, an analysis of the precision of LIDAR range measurements is presented. LIDAR data from two different sensors (HLS-LFCD-LDS and SICK TIM561) were analyzed regarding the influence of range, incident angle to the surface, and material. Based on the results, a data-driven model for LIDAR precision behavior was developed, and a comparison with standard deviation models based on the vendor-provided specifications was presented. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.}, language = {en} } @article{SpiessStrobelKauppetal., author = {Spiess, Florian and Strobel, Norbert and Kaupp, Tobias and Kounev, Samuel}, title = {A data-driven Model for Range Sensors}, series = {Encyclopedia with Semantic Computing and Robotic Intelligence}, journal = {Encyclopedia with Semantic Computing and Robotic Intelligence}, language = {en} }