Autonomous Exploration for 3D Map Learning

Joho, Dominik; Stachniss, Cyrill; Pfaff, Patrick; Burgard, Wolfram

doi:10.1007/978-3-540-74764-2_4

Cited by 32 publications

(20 citation statements)

References 11 publications

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“…In [17], the authors employed an action set composed of only three way points. A set of candidate destinations is evaluated in [19,73,102,120,138,156]. Sim et al [148,149] use a greedy strategy to generate policies in the form of short trajectories.…”

Section: Action Selectionmentioning

confidence: 99%

Mapping, Planning and Exploration with Pose SLAM

Valencia,

Andrade-Cetto

2018

Springer Tracts in Advanced Robotics

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This thesis reports research on mapping, path planning, and autonomous exploration. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common SLAM approach, adopting Pose SLAM as the basic state estimation machinery. The main contribution of this thesis is an approach that allows a mobile robot to plan a path using the map it builds with Pose SLAM and to select the appropriate actions to autonomously construct this map.Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. In Pose SLAM, observations come in the form of relative-motion measurements between robot poses. With regards to extending the original Pose SLAM formulation, this thesis studies the computation of such measurements when they are obtained with stereo cameras and develops the appropriate noise propagation models for such case. Furthermore, the initial formulation of Pose SLAM assumes poses in SE(2) and in this thesis we extend this formulation to SE(3), parameterizing rotations either with Euler angles and quaternions. We also introduce a loop closure test that exploits the information from the filter using an independent measure of information content between poses. In the application domain, we present a technique to process the 3D volumetric maps obtained with this SLAM methodology, but with laser range scanning as the sensor modality, to derive traversability maps that were useful for the navigation of a heterogeneous fleet of mobile robots in the context of the EU project URUS.Aside from these extensions to Pose SLAM, the core contribution of the thesis is an approach for path planning that exploits the modeled uncertainties in Pose SLAM to search for the path in the pose graph with the lowest accumulated robot pose uncertainty, i.e., the path that allows the robot to navigate to a given goal with the least probability of becoming lost. An added advantage of the proposed path planning approach is that since Pose SLAM is agnostic with respect to the sensor modalities used, it can be used in different environments and with i different robots, and since the original pose graph may come from a previous mapping session, the paths stored in the map already satisfy constraints not easy modeled in the robot controller, such as the existence of restricted regions, or the right of way along paths. The proposed path planning methodology has been extensively tested both in simulation and with a real outdoor robot.Our path planning approach is adequate for scenarios where a robot is initially guided during map construction, but autonomous during execution. For other scenarios in which more autonomy is required, the robot should be able to explore the environment without any supervision. The second core contribution of this thesis is an autonomous exploration method that complements the aforementioned path planning strategy. The method selects the appropriate...

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Section: Action Selectionmentioning

confidence: 99%

Mapping, Planning and Exploration with Pose SLAM

Valencia,

Andrade-Cetto

2018

Springer Tracts in Advanced Robotics

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“…In particular, a large body of work focuses on reconstructing the 3D structure of the environment which is a key information for several tasks such as victim detection and localization in search and rescue [8], [6], [7]. In this scenario, most previous approaches focus on the use of dense sensors that can provide accurate information from the environments such as 2D or 3D laser range finders [14], [15], [16], or more recently the Kinect system [6]. Much of this work is based on exploration strategies for single and multi-robot systems that aim at maximising the information that each robot can acquire given the next possible moves.…”

Section: Related Workmentioning

confidence: 99%

Visual coverage using autonomous mobile robots for search and rescue applications

Bue

Tamassia

Signorini

et al. 2013

2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Abstract-This paper focuses on visual sensing of 3D largescale environments. Specifically, we consider a setting where a group of robots equipped with a camera must fully cover a surrounding area. To address this problem we propose a novel descriptor for visual coverage that aims at measuring visual information of an area based on a regular discretization of the environment in voxels. Moreover, we propose an autonomous cooperative exploration approach which controls the robot movements so to maximize information accuracy (defined based on our visual coverage descriptor) and minimizing movement costs. Finally, we define a simulation scenario based on real visual data and on widely used robotic tools (such as ROS and Stage) to empirically evaluate our approach. Experimental results show that the proposed method outperforms a baseline random approach and an uncoordinated one, thus being a valid solution for visual coverage in large scale outdoor scenarios.

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“…Finding the next best view in such data can be very hard, as it does not supply any information about geometric structures such as corners, edges, surfaces and normals. In contrast to many other algorithms ( [5], [10]), ours does not require such information.…”

Section: Algorithmmentioning

confidence: 99%

“…While the implementations of SLAM have improved greatly in recent years, one aspect of mapping three-dimensional environments was given comparatively little attention: finding the next best view. Typically, SLAM algorithms have been researched and developed on mobile platforms moving on flat surfaces [10], [11], [5]. This setup does not necessitate high efficiency in exploration, as it does not inherently impose time constraints.…”

Section: Introductionmentioning

confidence: 99%

Towards autonomous airborne mapping of urban environments

Adler¹,

Xiao²

2012

2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

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Abstract-This work documents our progress on building an unmanned aerial vehicle capable of autonomously mapping urban environments. This includes localization and tracking of the vehicle's pose, fusion of sensor-data from onboard GNSS receivers, IMUs, laserscanners and cameras as well as realtime path-planning and collision-avoidance. Currently, we focus on a physics-based approach to computing waypoints, which are subsequently used to steer the platform in three-dimensional space. Generation of efficient sensor trajectories for maximized information gain operates directly on unorganized point clouds, creating a perfect fit for environment mapping with commonly used LIDAR sensors and time-of-flight cameras. We present the algorithm's application to real sensor-data and analyze its performance in a virtual outdoor scenario.

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Autonomous Exploration for 3D Map Learning

Cited by 32 publications

References 11 publications

Mapping, Planning and Exploration with Pose SLAM

Mapping, Planning and Exploration with Pose SLAM

Visual coverage using autonomous mobile robots for search and rescue applications

Towards autonomous airborne mapping of urban environments

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