Localization method for mobile robots based on landmarks

• US 8,930,127 B2
• Filed: 07/13/2008
• Issued: 01/06/2015
• Est. Priority Date: 07/12/2007
• Status: Active Grant

• Current Assignee: Haifa University Economic Corporation Ltd, Haifa University Research Corp., Ltd., TECHNION RESEARCH AND DEVELOPMENT FOUNDATION, LTD.
• Original Assignee: TECHNION RESEARCH AND DEVELOPMENT FOUNDATION, LTD., Carmel-Haifa University Economic corp. Ltd. (University of Haifa)
• Inventors: Shimshoni, Ilan, Loevsky, Igal
• Primary Examiner(s): PAULS, JOHN A
• Application Number:
  US12/668,521
• Publication Number:
  US 20100256908A1
• Time in Patent Office: 2,368 Days
• Field of Search: 701/300
• US Class Current: 701/300
• CPC Class Codes: G01S 17/875 for determining attitude
• CPC Class Codes: G05D 1/0236 in combination with a laser lasers per se H01S

Abstract

A method for estimating the location and orientation of a mobile robot with respect to landmarks whose positions are given using a sensor for measuring bearings of the landmarks with respect to the robot, where bias errors are present. The method comprises using the sensor to obtain bearings the landmarks. The location and orientation of the robot with respect to the landmarks is estimated, based on the measured bearings, and bias errors associated with the measured bearings are corrected to obtain corrected bearings. A correction function is used to finds the minimal sum of squared errors between measured bearings and the corrected bearings. The location and the orientation of the robot is determined to be where the sum of squared errors between measured bearings and the corrected bearings is minimal.

Claims

1. A method for estimating the location and orientation of a mobile robot provided with a sensor for measuring bearings of landmarks with respect to the robot, the method comprising:

using the sensor to measure bearings to a scanned subset of the landmarks, positions of the landmarks being given;

estimating the location and orientation of the robot with respect to the landmarks of the scanned subset based on the measured bearings;

calculating a calculated bearing to each of the landmarks of the scanned subset based on the estimated location and orientation of the robot and on the given positions of the landmarks;

finding a correction function for each of the landmarks such that when the correction function is applied to the bearing measurements to the landmarks of the scanned subset to yield corrected bearings to the landmarks of the scanned subset, when a corrected location of the robot is calculated using the corrected bearings, and when a calculated bearing is calculated using the corrected location, an error value between the measured bearings and the corrected bearings is minimized; and

applying the found correction functions to bearing measurements to the landmarks to correct bias errors in the bearing measurements by the sensors so as to determine a corrected location and the orientation of the robot.

2. The method as claimed in claim 1, wherein the error value a sum of squares of differences between the measured bearings and the corrected bearings.

3. The method as claimed in claim 1, wherein the bias errors associated with the measured bearings comprise errors caused by a mechanical deficiency of the sensor.

4. The method as claimed in claim 1, wherein the corrected bearing comprises a corrected angle of bearing to each of the landmarks.

5. The method as claimed in claim 4, wherein the correction function denoted ƒ

is defined to be continuous and wherein the corrected bearing β

_corri for each of the landmarks is;

β

_corrii=β

_i+ƒ

(β

_i)

where β

_i is the angle of a measured bearing for each of the landmarks.

6. The method as claimed in claim 1, wherein the given positions of the landmarks were inaccurately determined and the bias errors associated with the measured bearings comprise errors associated with the inaccurate determination of the positions of the landmarks.

7. The method as claimed in claim 6 wherein using the sensor comprises using the sensor to obtain several bearing sets, each bearing set including bearings of at least three landmarks, as the robot changes its position, the method further comprising correcting the given positions by calculating optimal positions of the landmarks that minimize the error value and finding a similarity transformation that when applied to the optimal positions yields transformed positions of the landmarks that are closest to the given positions of the landmarks.

8. The method as claimed in claim 1, wherein the bias errors associated with the measured bearings comprise errors associated with a mechanical deficiency of the sensor and errors associated with inaccurate determination of the positions of the landmarks.

9. The method as claimed in claim 1, wherein the bias errors associated with the measured bearings comprise errors attributed to rotations of the robot.

10. The method as claimed in claim 9, wherein the correction function is used to estimate a rotation speed of the robot that minimizes error values attributed to the rotations of the robot.

11. The method as claimed in claim 1, wherein the bias errors associated with the measured bearings comprise errors attributed to misidentification of the landmarks.

12. The method as claimed in claim 11, comprising:

determining existence of misidentified landmarks if the error value is above a predetermined threshold;

for a randomly chosen subset of the landmarks determining the location and the orientation of the robot to be where the error value is minimal;

calculating the error value for the landmarks that were not included in the subset;

if for most of the landmarks the calculated error value is below a predetermined value, identifying and disregarding landmarks where the errors were above the predetermined value as the misidentified landmarks, and if for most of the landmarks the error value is above a predetermined value, randomly choosing a new subset of the landmarks and repeating the steps of determining the location and the orientation of the robot to be where the error value is minimal and calculating the error value for the landmarks that were not included in the new subset until the error value for most of the landmarks is below the predetermined value.