Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

Hu, Guanghui; Zhang, Weizhi; Wan, Huawei; Li, Xinxin

doi:10.3390/s20061578

Cited by 24 publications

(14 citation statements)

References 30 publications

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“…Some articles attempted to compensate the heading drift by designing a novel AHRS model. Designing an EKF-based attitude error estimation system [ 16 ], attitude accuracy improvement using decision tree [ 17 ], and RF signals [ 18 ] are discussed and experimented with. In these works, the heading drift compensation is based on the proper antenna and receiver design; however, the methods have been performed in short-term experiments in which their robustness and stability were not proved.…”

Section: Related Workmentioning

confidence: 99%

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

Farhangian

Sefidgar

Landry

2021

Sensors

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The advancement of indoor Inertial Navigation Systems (INS) based on the low-cost Inertial Measurement Units (IMU) has been long reviewed in the field of pedestrian localization. There are various sources of error in these systems which lead to unstable and unreliable positioning results, especially in long term performances. These inaccuracies are usually caused by imperfect system modeling, inappropriate sensor fusion models, heading drift, biases of IMUs, and calibration methods. This article addresses the issues surrounding unreliability of the low-cost Micro-Electro-Mechanical System (MEMS)-based pedestrian INS. We designed a novel multi-sensor fusion method based on a Time of Flight (ToF) distance sensor and dual chest- and foot-mounted IMUs, aided by an online calibration technique. An Extended Kalman Filter (EKF) is accounted for estimating the attitude, position, and velocity errors, as well as estimation of IMU biases. A fusion architecture is derived to provide a consistent velocity measurement by operative contribution of ToF distance sensor and foot mounted IMU. In this method, the measurements of the ToF distance sensor are used for the time-steps in which the Zero Velocity Update (ZUPT) measurements are not active. In parallel, the chest mounted IMU is accounted for attitude estimation of the pedestrian’s chest. As well, by designing a novel corridor detection filter, the heading drift is restricted in each straightway. Compared to the common INS method, developed system proves promising and resilient results in two-dimensional corridor spaces for durations of up to 11 min. Finally, the results of our experiments showed the position RMS error of less than 3 m and final-point error of less than 5 m.

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Section: Related Workmentioning

confidence: 99%

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

Farhangian

Sefidgar

Landry

2021

Sensors

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“…A challenging aspect is heading estimation. Most approaches rely on using only the gyroscope (delivering good heading accuracy in a short time interval, but prone to drift over time due to integration errors) [ 14 , 15 , 16 ] or the fusion of the gyroscope with the magnetometer (delivering absolute headings, but prone to large errors due to the presence of iron materials in indoor environments) [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Cock

Joseph

Martens

et al. 2021

Sensors

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We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

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“…Although the results provided are robust against environmental changes, this technique is prone to error accumulation [9]. Kalman filters have been expensively used in order to improve accuracy [10].…”

Section: Introductionmentioning

confidence: 99%

From Robot Self-Localization to Global-Localization: An RSSI Based Approach

Lentzas¹,

Vrakas²

2021

Preprint

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Localization is a crucial task for autonomous mobile robots in order to successfully move to goal locations in their environment. Usually this is done in a robot-centric manner, where the robot maintains a map with its body in the center. In swarm robotics applications, where a group of robots need to coordinate in order to achieve their common goals, robot-centric localization will not suffice as each member of the swarm has its own frame of reference. One way to deal with this problem is to create, maintain and share a common map (global coordinate system), among the members of the swarm. This paper presents an approach to global localization for a group of robots in unknown, GPS and landmark free environments that extends the localization scheme of the LadyBug algorithm [1]. The main idea relies on members of the swarm stay still and act as beacons, emitting electromagnetic signals. These stationary robots form a global frame of reference and the rest of the group localize themselves in it using the received signal strength indicator (RSSI). The proposed method is evaluated, and the results obtained from the experiments are promising.

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Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter

Cited by 24 publications

References 30 publications

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

Applying a ToF/IMU-Based Multi-Sensor Fusion Architecture in Pedestrian Indoor Navigation Methods

Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

From Robot Self-Localization to Global-Localization: An RSSI Based Approach

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