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Mobile Robot Traversability Mapping: For Outdoor Navigation
Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, The Institute of Technology.
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To avoid getting stuck or causing damage to a vehicle or its surroundings a driver must be able to identify obstacles and adapt speed to ground conditions. An automatically controlled vehicle must be able to handle these identifications and adjustments by itself using sensors, actuators and control software. By storing properties of the surroundings in a map, a vehicle revisiting an area can benefit from prior information.

Rough ground may cause oscillations in the vehicle chassis. These can be measured by on-board motion sensors. For obstacle detection, a representation of the geometry of the surroundings can be created using range sensors. Information on where it is suitable to drive, called traversability, can be generated based on these kinds of sensor measurements.

In this work, real semi-autonomous mobile robots have been used to create traverasbility maps in both simulated and real outdoor environments. Seeking out problems through experiments and implementing algorithms in an attempt to solve them has been the core of the work.

Finding large obstacles in the vicinity of a vehicle is seldom a problem; accurately identifying small near-ground obstacles is much more difficult, however. The work additionally includes both high-level path planning, where no obstacle details are considered, and more detailed planning for finding an obstacle free path. How prior maps can be matched and merged in preparation for path planning operations is also shown. To prevent collisions with unforeseen objects, up-to-date traversability information is used in local-area navigation and obstacle avoidance.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. , 122 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1564
Keyword [en]
Traversability, Laser, Roughness, Mapping, Planning, Mobile robot, Navigation, Implementation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-85937Local ID: LIU-TEK-LIC-2012:49ISBN: 978-91-7519-726-5 (print)OAI: oai:DiVA.org:liu-85937DiVA: diva2:573893
Presentation
2012-12-14, A39, A-huset, Campus Valla, Linköpings universitet, Linköping, 14:15 (English)
Opponent
Supervisors
Available from: 2012-12-03 Created: 2012-12-03 Last updated: 2012-12-06Bibliographically approved
List of papers
1. Sensor Data Fusion for Terrain Exploration by Collaborating Unmanned Ground Vehicles
Open this publication in new window or tab >>Sensor Data Fusion for Terrain Exploration by Collaborating Unmanned Ground Vehicles
2008 (English)In: Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, Cologne, Germany, 30th June–3rd July, IEEE Xplore , 2008, 1214-1221 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents work in progress for the pre-Runners project. The goal is to experimentally demonstrate the value of unmanned ground vehicles (UGVs) in collaboration with a main vehicle in an outdoor setting. With uneven terrain and unexpected obstacles the main vehicle benefits from a priori information of the terrain ahead. This information can be gathered by a smaller, more agile, and risks tolerant autonomous “prerunner”. The results presented, represent the first steps toward the important task of determining the traversable surfaces and communicating the results within the team. The information sharing between vehicles is based on Collaborative Smoothing and Mapping (C-SAM). The horizontal position is also estimated within the C-SAM. In parallel the vertical component and orientation is estimated by a filter fusing data from odometry, an imu and two lasers to allow computation of traversability maps to be shared within the team.

Place, publisher, year, edition, pages
IEEE Xplore, 2008
Keyword
Collaboration, SLAM, SAM, sensor data fusion, data association, Kalman filtering, traversability estimation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-15323 (URN)10.1109/ICIF.2008.4632348 (DOI)978-3-8007-3092-6 (ISBN)
Conference
11th International Conference on Information Fusion, FUSION 2008, Cologne, Germany, 30th June–3rd July
Available from: 2008-10-31 Created: 2008-10-31 Last updated: 2012-12-03Bibliographically approved
2. Results of the TAIS/preRunners-project
Open this publication in new window or tab >>Results of the TAIS/preRunners-project
2009 (English)In: Fourth Swedish Workshop on Autonomous Robotics SWAR'09 / [ed] Lars Asplund, 2009, 60-61 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents selected results of the preRunners project. The goal was to experimentally demonstrate the value of collaborating unmanned ground vehicles (UGVs) in an outdoor setting. With uneven terrain and unexpected obstacles a large vehicle benefits greatly from a priori information about theterrain ahead. This information can be gathered by a smaller, more agile and risk tolerant autonomous “preRunner”.

Keyword
Outdoor robotics, Mapping, Traversability, SLAM, C-SLAM
National Category
Information Science
Identifiers
urn:nbn:se:liu:diva-52684 (URN)
Conference
Fourth Swedish Workshop on Autonomous Robotics SWAR'09, Västerås, Sweden, September 8
Projects
PreRunners
Available from: 2010-01-08 Created: 2010-01-08 Last updated: 2012-12-03Bibliographically approved
3. Local Navigation using Traversability Maps
Open this publication in new window or tab >>Local Navigation using Traversability Maps
2010 (English)In: 7th Symposium on Intelligent Autonomous Vehicles, IAV2010, Lecce, Volume 7, Part 1 / [ed] Indiveri, Giovanni, Pascoal, Antonio M., 2010, 324-329 p.Conference paper, Published paper (Refereed)
Abstract [en]

In outdoor robotics it is important to be able to ascertain the traversability of thes urrounding terrain. This paper presents a system where continuously generated traversability maps, useful for obstacle avoidance, are generated, stored and later reused to perform detailed local path planning. The detailed plan can be used as a temporary replacement for parts of a global plan that may lack knowledge about impassable obstacles or troublesome areas. The paper also describes an algorithm useful for on-line alignment and merging of previously stored traversability maps. Being able to align and merge maps is vital as the estimated global poses of multiple overlapping maps stored at different times may differ.

Keyword
Obstacle avoidance, local navigation, traversability, path planning
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-60739 (URN)10.3182/20100906-3-IT-2019.00057 (DOI)978-3-902661-87-6 (ISBN)
Conference
Intelligent Autonomous Vehicles, September 6-8, University of Salento, Lecce, Italy, Volume7, Part 1
Available from: 2010-10-25 Created: 2010-10-25 Last updated: 2014-09-22
4. Multi Layered Maps for Enhanced Environmental Perception
Open this publication in new window or tab >>Multi Layered Maps for Enhanced Environmental Perception
2011 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Traditionally, an in-vehicle map consists of only one type of data, tailored for a single user function. For example, the navigation maps contain spatial information about the roads. On the other hand, a map built for adaptive cruise control use consists of the detected vehicles and their properties. In autonomous vehicle research, the maps are often built up as an occupancy grid where areas are classified as passable or impassable. Using these kinds of maps separately, however, is not enough to support the traffic safety enhancing and advanced driver assistance systems of today and tomorrow.

Instead of using separate systems to handle individual safety or planning tasks, information could be stored in one shared map containing several correlated layers of information. Map information can be collected by any number of different sensor devices, and fusion algorithms can be used to enhance the quality of the information. User functions that base their decisions on the multi-layered map can then retrieve any subset of the stored information making them scalable in terms of processor and memory use.

The advantages of using a shared multi-layer spatial data storage are several:

Sensors and user functions are decoupled. This can make it easier and more cost efficient to implement additional functions.

Data quality is enhanced. Since fusion techniques can be used to generate estimates of physical properties from several sensors, the fused data is based on all available information.

Using models that describe a certain entity, properties that are not even measured can be estimated by the system.

This work describes an experimental semi-autonomous ground vehicle system, where on-line generated maps containing multiple layers of information are used for obstacle avoidance and planning of a suitable path between waypoints. The system is primarily simulated using physical vehicle models in a suitable environment, but limited real-world experiments with a subset of functions are also performed. 

National Category
Robotics Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-70851 (URN)10.4271/2011-01-2244 (DOI)
Conference
SAE 2011 Commercial Vehicle Engineering Congress, 13-14 September 2011, Rosemont, Illinois USA
Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2012-12-03Bibliographically approved

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Nordin, Peter

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