Sensor Management in 2D Lidar-Based Underground Positioning

Abstract

Lidar-based positioning in a 2D map is analyzed as a method to provide a robust, high accuracy, and infrastructurefree positioning system required by the automation development in underground mining. Expressions are derived that highlight separate information contributions to the obtained position accuracy. This is used to develop two new methods that efficiently select which subset of available lidar rays to use to reduce the computational complexity and allow for online processing with minimal loss of accuracy. The results are verified in simulations of a mid-articulated underground loader in a mine. The methods are shown to be able to reduce the number of rays needed without considerably affecting the performance, and to be competitive with currently used methods. Furthermore, simulations highlight the effects of errors in the map and other map properties, and how imperfect maps degrades the performance of different selection strategies.

Survey on 2D Lidar Feature Extraction for Underground Mine Usage

“…The orientation is then defined as in which circular sector the point p j lays in, and a discrete distance function d θj ,θ k is defined as how many sectors are between θ j and θ k . A cornerness score is then computed for each of the two subsets in (6) as the sum of all point pair distances in the subset…”

“…navigation system described in [4], but instead of matching the rays to locally created occupancy grid maps, the data is matched to a given global metric map of the operation area. The approach of matching raw lidar data is also used in the sensor selection methods described in [6], where the aim is to cut computational complexity. The methods are also compared to a standard ICP variant using the point-to-plane error metric without outlier rejection.…”

“…Existing commercial autonomous mine vehicles are equipped with 2D lidars, odometry and inertial measurement units (IMUs) [2][3][4][5], and fuses the information from the different data sources by filtering algorithms to determine the position of the vehicle in a map. Matching all laser rays from a lidar scan individually to a predefined map is computationally expensive, and methods for selecting sub-sets of available rays without losing estimation precision are developed in [6]. However, these methods are sensitive to outliers in the data and imperfections in the map.…”

Robust LIDAR-Based Localization in Underground Mines

“…• Chapter 4 is based on the publication [77] and considers the problem of selecting a subset of available measurements from a 2D lidar scan. To decrease processing time, without losing performance of the resulting state estimate, the information contribution from each individual laser ray is analyzed.…”