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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2006.06.007 Byline: Giorgio Grisetti (a)(b), Gian Diego Tipaldi (b), Cyrill Stachniss (c)(a), Wolfram Burgard (a), Daniele Nardi (b) Abstract: Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao-Blackwellized particle filters. Author Affiliation: (a) University of Freiburg, Department of Computer Science, D-79110 Freiburg, Germany (b) Dipartimento Informatica e Sistemistica, Universita 'La Sapienza', I-00198 Rome, Italy (c) Eidgenossische Technische Hochschule Zurich (ETH), IRIS, 8092 Zurich, Switzerland Article History: Received 1 October 2005; Revised 1 April 2006; Accepted 1 June 2006

Abstract Currently, fully automated as-built modeling of building interiors using point-cloud data still remains an open challenge, due to several problems that repeatedly arise: (1) complex indoor environments containing multiple rooms; (2) time-consuming and labor-intensive noise filtering; (3) difficulties of representation of volumetric and detail-rich objects such as windows and doors. This study aimed to overcome such limitations while improving the amount of details reproduced within the model for further utilization in BIM. First, we input just the registered three-dimensional (3D) point-cloud data and segmented the point cloud into separate rooms for more effective performance of the later modeling phases for each room. For noise filtering, an offset space from the ceiling height was used to determine whether the scan points belonged to clutter or architectural components. The filtered points were projected onto a binary map in order to trace the floor-wall boundary, which was further refined through subsequent segmentation and regularization procedures. Then, the wall volumes were estimated in two ways: inside- and outside-wall-component modeling. Finally, the wall points were segmented and projected onto an inverse binary map, thereby enabling detection and modeling of the hollow areas as windows or doors. The experimental results on two real-world data sets demonstrated, through comparison with manually-generated models, the effectiveness of our approach: the calculated RMSEs of the two resulting models were 0.089 m and 0.074 m, respectively. [ABSTRACT FROM AUTHOR]

Industry demands flexible robots that are able to accomplish different tasks at different locations such as navigation and mobile manipulation. Operators often require mobile robots operating on factory floors to follow definite and predictable behaviors. This becomes particularly important when a robot shares the workspace with other moving entities. In this paper, we present a system for robot navigation that exploits previous experiences to generate predictable behaviors that meet user’s preferences. Preferences are not explicitly formulated but implicitly extracted from robot experiences and automatically considered to plan paths for the successive tasks without requiring experts to hard-code rules or strategies. Our system aims at accomplishing navigation behaviors that follow user’s preferences also to avoid dynamic obstacles. We achieve this by considering a probabilistic approach for modeling uncertain trajectories of the moving entities that share the workspace with the robot. We implemented and thoroughly tested our system both in simulation and on a real mobile robot. The extensive experiments presented in this paper demonstrate that our approach allows a robot for successfully navigating while performing predictable behaviors and meeting user’s preferences. [ABSTRACT FROM AUTHOR]

A clean and reliable map of the environment is key for a variety of robotic tasks including localization, path planning, and navigation. Dynamic objects are an inherent part of our world, but their presence often deteriorates the performance of various mapping algorithms. This not only makes it important but necessary to remove these dynamic points from the map before they can be used for other tasks such as path planning. In this paper, we address the problem of building maps of the static aspects of the world by detecting and removing dynamic points from the source point clouds. We target a map cleaning approach that removes the dynamic points and maintains a high quality map of the static part of the world. To this end, we propose a novel offline ground segmentation method and integrate it into the OctoMap to better distinguish between the moving objects and static road backgrounds. We evaluate our approach using SemanticKITTI for both, dynamic object removal and ground segmentation algorithms as well as on the Apollo dataset. The evaluation results show that our method outperforms the baseline methods in both tasks and achieves good performance in generating clean maps over different datasets without any change in the parameters. [ABSTRACT FROM AUTHOR]

Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution. • We propose an adaptive planning algorithm for UAVs in semantic segmentation scenarios. • Our new decision function allows for dynamically changing the UAV altitude to focus on areas of interest. • Experiments using real-world data demonstrate the efficacy and generality of our solution. [ABSTRACT FROM AUTHOR]

Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learning-based pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other state-of-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach. [ABSTRACT FROM AUTHOR]