Efficient traversability analysis for mobile robots using the Kinect sensor

Abstract

Abstract-For autonomous robots, the ability to classify their local surroundings into traversable and non-traversable areas is crucial for navigation. In this paper, we address the problem of online traversability analysis for robots that are only equipped with a Kinect-style sensor. Our approach processes the depth data at 10 fps-25 fps on a standard notebook computer without using the GPU and allows for robustly identifying the areas in front of the sensor that are safe for navigation. The component presented here is one of the building blocks of the EU project ROVINA that aims at the exploration and digital preservation of hazardous archeological sites with mobile robots. Real world evaluations have been conducted in controlled lab environments, in an outdoor scene, as well as in a real, partially unexplored, and roughly 1700 year old Roman catacomb.

Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming

“…Estimating semantic information from sensor data is a relevant topic in robotics (Milioto & Stachniss, ) and computer vision (Leibe, Leonardis, & Schiele, ), since more than two decades (Papageorgiou, Oren, & Poggio, ). Various approaches have been proposed to analyze the environment around a robot in indoor (Stachniss, Martínez‐Mozos, Rottmann, & Burgard, ) as well as outdoor for optimizing mapping, traversability analysis (Bogoslavskyi, Vysotska, Serafin, Grisetti, & Stachniss, ; Wurm, Kretzschmar, Kümmerle, Stachniss, & Burgard, ), and navigation (Kümmerle, Ruhnke, Steder, Stachniss, & Burgard, ), for pedestrian detection (Leibe, Seemann, & Schiele, ), for autonomous driving (Behley, Steinhage, & Cremers, ), for face detection (Viola & Jones, ), and for various other applications. In the past, a variety of classification techniques such as Boosting methods (Freund & Schapire, ), support vector machines (SVM; Boser, Guyon, & Vapnik, ), or random forests (Breiman, ) have been applied.…”

A new approach to digitalization and data management of cultural heritage sites

“…We have devoted a considerable effort in making the approach more robust and developing an extension of DCS, originally proposed by Agarwal et al [3], to assessing the degree of consistency of maps [5] and to automatically calibrating the sensors (see also [6], [7]). In order to safely navigate in the environment, the robot uses an abstract 2D representation of the environment called traversability map [8] that, when coupled with exploration techniques that consider the expected gain of novel information (see [9] for an overview tutorial), allows for a safe and exhaustive survey of the environment.…”

Robotics and Virtual Reality for Cultural Heritage Digitization And Fruition

“…A considerable effort has been devoted in making the approach more robust and developing an extension of DCS, originally proposed by Agarwal et al Agarwal et al (2014), to assessing the degree of consistency of maps Mazuran et al (2014) and to automatically calibrating the sensors (see also Basso et al 2014;Tedaldi et al 2014). In order to safely navigate in the environment, the robot uses an abstract 2D representation of the environment called traversability map Bogoslavskyi et al (2013) that, when coupled with exploration techniques that consider the expected gain of novel information (see Stachniss and Burgard 2012 for an overview tutorial), allows for a safe and exhaustive survey of the environment.…”