Development of Localization Method Using Magnetic Sensor and LIDAR

Abstract

In mobile robot's localization, it is well known that odometry can provide a reliable accuracy in short term navigation and a very high sampling rate. However, odometry produces cumulative error because of uneven terrains or wheel slippage and this error increases proportionally with the distance traveled by the mobile robot. Therefore, it is necessary to augment odometry with other sensors to improve its accuracy. This paper proposes an estimation method of mobile robot orientation using an environmental magnetic field (magnetic field that occurs in the environment). A three-axis magnetic sensor is utilized to scan the environmental magnetic field to build a magnetic database on a grid map called "a magnetic map" with the mobile robot operated with a joystick on a desired route. The mobile robot then estimates its orientation by comparing the magnetic sensor readings with the magnetic data stored in the magnetic map. However, even if the proposed method can improve the accuracy of the odometry, positioning error still remains as a major problem in long term navigation. In this work, a localization method using Monte Carlo Localization (MCL) based on a Light Detection and Ranging (LIDAR) is utilized to fix the positioning error at the areas where landmark can be observed. The experimental results showed that the mobile robot could localize robustly in any environments with the proposed method.

Multisensor robust localization in various environment with correlation checking test

“…The first fusion approach is that a user assigns localization sensors to the environments where a robot moves in, according to their experiences and knowledge [18]. While this approach is done easily if the robot moves a limited area, the workload becomes harder as the area becomes larger.…”

“…Localization, estimating a device position based on onboard sensor data, has been researched for vehicles, smartphones, autonomous mobile robots, etc. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For example, as for autonomous mobile robots, position information is used for map construction, path planning, and object avoidance [21][22][23][24][25][26][27][28][29].…”