Exploration and mapping with mobile robots

Models of the environment are needed for a wide range of robotic <br>applications, from search and rescue to automated vacuum cleaning. <br>Learning maps has therefore been a major research focus in the <br>robotics community over the last decades. Robots that are able to <br>acquire an accurate model of their environment on their own are <br>regarded as fulfilling a major precondition of truly autonomous <br>agents. In order to solve the map learning problem, a robot has to <br>address mapping, localization, and path planning at the same time. In <br>general, these three tasks cannot be decoupled and solved <br>independently and map learning is thus referred to as the simultaneous <br>planning, localization, and mapping problem. Because of the coupling <br>between these tasks, this problem is very complex. It can become even <br>more complex when there are dynamic changes in the environment or <br>several robots are being used together to solve the problem. <br> <br>The contributions of this thesis are solutions to various aspects of <br>the autonomous map learning problem. We first present approaches to <br>exploration that take into account the uncertainty in the world model <br>of the robot. We then describe how to achieve good collaboration <br>among a team of robots so that they efficiently solve an exploration <br>task. Our approach distributes the robots over the environment and in <br>this way avoids redundant work and reduces the risk of interference <br>between the individual team members. We furthermore provide a <br>technique to make use of background knowledge about typical <br>spacial structures when distributing the robots over the <br>environment. As a result, the overall time needed to complete the <br>exploration mission is reduced. <br> <br> <br>To deal with the uncertainty in the pose of a robot, we present a <br>solution to the simultaneous localization and mapping problem. The <br>difficulty in this context is to build up a map while at the same time <br>localizing the robot in this map. Our approach maintains a joint <br>posterior about the trajectory of the robot and the model of the <br>environment. It produces highly accurate maps in an efficient and <br>robust way. <br> <br>In this thesis, we address step-by-step the different problems in the <br>context of map learning and integrate our techniques into a single <br>system. We provide an integrated approach that simultaneously deals <br>with mapping, localization, and path planning. It seeks to minimize <br>the uncertainty in the map and in the trajectory estimate based on the <br>expected information gain of future actions. It takes into account <br>potential observation sequences to estimate the uncertainty reduction <br>in the world model when carrying out a specific action. Additionally, <br>we focus on mapping and localization in non-static environments. Our <br>approach allows a robot to consider different spatial configurations <br>of the environment and in this way makes the pose estimate more robust <br>and accurate in non-static worlds.

Author dc:creator

• Stachniss, Cyrill

Contributors dc:contributor

• Burgard, Wolfram

• exploration, mapping, mobile robot, SLAM, localization

Repository record source_url

https://freidok.uni-freiburg.de/data/2440

OAI identifier oai:identifier

oai:freidok.uni-freiburg.de:2440