Magnetic Anomaly-Matched Trajectory and Dead Reckoning Fusion Mobile Robot Navigation
Yong Hun Kim, Bo Sung Ko, and Jin Woo Song*Department of Intelligent Mechatronics Engineering and Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul, Korea*Corresponding author: jwsong@sejong.ac.kr

Background and Objectives
Background

Objectives & Contributions• Recent studies mainly focus on suppressing positional
divergence to achieve acceptable navigation accuracy indoor
and do not consider the cost.

• In this study, to solve the low position accuracy problem, the position
result calculated through magnetic anomaly localization is configured
as a trajectory and used for navigation.

• Considering the cost, the magnetic anomaly matching
localization method can be a superior solution.

• The calculated trajectory is compared with dead reckoning, whichcalculates using a mobile robot wheel spin and gyroscope. In
optimizing the trajectory, the navigation sensor error was calculated tocompensate for the navigation.

• But, owing to the similar steel structure inside the building,there are multiple distorted magnetic fields with similarities,
which results in position errors.
• For this reason, magnetic-based navigation is not a major use.

• The proposed algorithm was verified using an open dataset, and as aresult, it was confirmed that can implement a stable and accurateindoor navigation algorithm at an inexpensive price.

Key-frame detection and error compensation

Magnetic Anomaly-matched trajectory• In the case of the turning section, wheel slip occurs significantly.
Thus, the odometry scale factor error and gyro bias are compensatedthrough trajectory information.

Transition probability𝑎=𝑒𝑥 𝑝( −𝑴𝒂𝒑𝑴𝒂𝒑)

𝐌𝐚𝐩( ) is a matrix that magnetic map• The turning and straight sections were detected according to thegradient change in the magnetic anomaly-matched trajectory.
Optimization function

𝑵, 𝒋 is a matrix index representing a row
𝜎 set

3 (the variance according to the distance)𝑒(𝑜 , 𝑏𝑔) = 𝑎𝑟𝑔𝑚𝑖𝑛 𝐌𝒕𝒓 − 𝐃𝒕𝒓,𝐌𝒕𝒓 is magnetic anomaly-matched trajectory
𝐃𝒕𝒓 is the trajectory calculated by DR
𝒐𝒔 is odometry scale factor
𝒃𝒈 is gyro bias

Emission probability𝑏 =𝑒𝑥 𝑝( −𝐌𝐚𝐠)

(a)

(b)

𝑚𝑎𝑔 is the magnetic field measured.
𝜎 is 𝑣𝑎𝑟(𝐌𝐚𝐠)

Forward probabilitys 𝑗 =Σs• Fig. 1 shows the HMM in which each parameter is connected at
the current time t and previous time t-1. Each hidden parameterof the HMM was selected as a magnetic map grid point within 3
m based on the previous location.
• After calculating the parameter probability, and decoding using
the Viterbi algorithm to form the trajectory.
• The trajectory is constructed by the grid points with themaximum probability among the forward probabilities.

• When the straight is detected,perform the EKF measurement
update using a magneticmatched position.
• When the turning motion isdetected, perform optimizationfor estimating the sensor error.
EKF state and system model𝛿𝐪 = 𝛿𝐩 𝛿𝜓 𝛿𝑣 𝑏00
𝐅= 000

Simulation results• The proposed algorithm was verified using the MagPIE dataset[Ref: https://ieeexplore.ieee.org/abstract/document/8115961].
• The algorithm accuracy was analyzed for three trajectories out of
the entire dataset, and the magnetic map grid size was selected
as 0.2 m.
SensorIMU
Magnetometer
For the ground truth
CameraLiDAR

Magnetic matched trajectory and DR fusion𝑖 𝑎 𝑏Example
Invensense MPU-6500
NXP MAG3110

Cost
$3.5
$1.46

Mv BlueFOX-MLC200w
Velodyne PUCK VLP-16$310$7,9990 𝑐𝑜𝑠(𝜓) 𝑣
0 −𝑠𝑖𝑛(𝜓) 𝑣
000000𝛿𝐪̇ = 𝐅δ𝐪 + 𝛏−𝜏 and −𝜏sin(𝜓)
0co s( 𝜓)
001−𝜏
00−𝜏

𝛏~𝑁(0, 𝐐)
represents Markov process model.

EKF measurement modelδ𝐳 = 𝐇δ𝐪 + 𝛆 = 𝐷𝑅 𝐷𝑅− 𝑀 𝑀

𝛆~𝑁(0, 𝐑)

𝐷𝑅 𝐷𝑅

is the position calculated by DR𝑀

is the position calculated by magnetic matching

𝑀Conclusion• The proposed algorithm can perform navigation accurately andstable by compensating for the error with an optimization method ina turning section with less accuracy.
• So that reason, when using the proposed technique, the DR
performance and localization accuracy of the magnetic anomaly
matching technique are improved.
• The price of the sensor used in this study is about 5 dollars including
the IMU and magnetometer. The system can be configured muchcheaper than using a camera or LiDAR.

Dataset1
Dataset2
Dataset3

DR (m)
7.40377.046518.708Proposed (m)0.2976
0.3572
0.5583

Intelligent Navigation & Control Systems Lab. Sejong University• If the proposed technique is used, the navigation systemconstruction cost is very low, and it is thought that it can be widelyused in fields where an indoor navigation solution is desired at a low
price.
• In the future, we plan to conduct more rigorous verification byconducting our own experiments.
https://sites.google.com/view/incsl Email: jwsong@sejong.ac.kr, Tel: 82)2-3408-3783