Title: Robust LIDAR-Based Localization in Underground Mines

Speaker: MSc Kristin Nielsen, Epiroc Drills AB/LiU

Opponent: Prof John Folkesson, KTH

Supervisor: Doc Gustaf Hendeby, LiU

Co-supervisors: Prof Fredrik Gustafsson, LiU, Dr Robert Lundh, Epiroc Drills AB/LiU

 

Date: 2021-05-28

Time: 13:15-15:00

Place: https://liu-se.zoom.us/j/68373726076?pwd=RUZRWDBBMlBCUTV3azBWNWNPbWIvZz09

Language: English

 

Abstract:

The mining industry is currently facing a transition from manually operated vehicles to remote or semi-automated vehicles. The vision is fully autonomous vehicles being part of a larger fleet, with humans only setting high-level goals for the autonomous fleet to execute in an optimal way. An enabler for this vision is the presence of robust, reliable and highly accurate localization. This is a requirement for having areas in a mine with mixed autonomous vehicles, manually operated vehicles, and unprotected personnel. The robustness of the system is important from a safety as well as a productivity perspective. When every vehicle in the fleet is connected, an uncertain position of one vehicle can result in the whole fleet begin halted for safety reasons.

Providing reliable positions is not trivial in underground mine  environments, where access to global satellite based navigation systems is denied. Due to the harsh and dynamically changing environment, onboard positioning solutions are preferred over systems utilizing external infrastructure. The focus of this thesis is localization systems relying only on sensors mounted on the vehicle, e.g., odometers, inertial measurement units, and 2D LIDAR sensors. The localization methods are based on the Bayesian filtering framework and estimate the distribution of the position in the reference frame of a predefined map     covering the operation area. This thesis presents research where the properties of 2D LIDAR data, and specifically characteristics when obtained in an underground mine, are considered to produce position estimates that are robust, reliable, and accurate.

First, guidelines are provided for how to tune the design parameters associated with the unscented Kalman filter (UKF). The UKF is an algorithm designed for nonlinear dynamical systems, applicable to this particular positioning problem. There exists no general guidelines for how to choose the parameter values, and using the standard values suggested in the literature result in unreliable estimates in the considered application. Results show that a proper parameter setup substantially improves the performance of this algorithm.

Next, strategies are developed to use only a subset of available measurements without losing quality in the position estimates. LIDAR sensors typically produce large amounts of data, and demanding real-time positioning information limits how much data the system can process. By analyzing the information contribution from each individual laser ray in a complete LIDAR scan, a subset is selected by maximizing the information content. It is shown how 80% of available LIDAR measurements can be dropped without significant loss of accuracy.

Last, the problem of robustness in non-static environments is addressed. By extracting features from the LIDAR data, a computationally tractable localization method, resilient to errors in the map, is obtained. Moving objects, and tunnels being extended or closed, result in a map not corresponding to the LIDAR observations. State-of-the art feature extraction methods for 2D LIDAR data are identified, and a localization algorithm is defined where features found in LIDAR data are matched to features extracted from the map. Experiments show that regions of the map containing errors are automatically ignored since no matching features are found in the LIDAR data, resulting in more robust position estimates.


Questions are answered by Ninna Stensgård, Phone  013-28 47 25

E-mail ninna.stensgard@liu.se


 

View all events
We use cookies to personalise content and ads, to provide social media features and to analyse our traffic. We also share information about your use of our site with our social media, advertising and analytics partners. View more
Cookies settings
Accept
Privacy & Cookie policy
Privacy & Cookies policy
Cookie name Active
The WASP website wasp-sweden.org uses cookies. Cookies are small text files that are stored on a visitor’s computer and can be used to follow the visitor’s actions on the website. There are two types of cookie:
  • permanent cookies, which remain on a visitor’s computer for a certain, pre-determined duration,
  • session cookies, which are stored temporarily in the computer memory during the period under which a visitor views the website. Session cookies disappear when the visitor closes the web browser.
Permanent cookies are used to store any personal settings that are used. If you do not want cookies to be used, you can switch them off in the security settings of the web browser. It is also possible to set the security of the web browser such that the computer asks you each time a website wants to store a cookie on your computer. The web browser can also delete previously stored cookies: the help function for the web browser contains more information about this. The Swedish Post and Telecom Authority is the supervisory authority in this field. It provides further information about cookies on its website, www.pts.se.
Save settings
Cookies settings