Ground vehicle localization based on inertial measurement signals

Gim, Juhui; Ahn, Changsun

doi:10.1109/iccas.2015.7364638

Cited by 2 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers have applied a map-based positioning method using only inertial sensors, not a dead reckoningbased positioning method [33][34][35][36][37][38][39]. The basic hypothesis of this method is that the inertial sensor signals imply vehicle responses to the road surface profile and the same road surface profile excites similar vehicle motions.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the inertial sensor signals in the distance domain can be the distinguishable features of positions. Pitch rate signals are mainly used for positioning [33][34][35][36][37], but roll rate signals are also used [38]. The proposed map-based approaches eliminate the error accumulation problem.…”

Section: Introductionmentioning

confidence: 99%

“…From the estimated road shapes, a spectrogram is constructed, which becomes the feature for positioning. The spectrogram-based feature requires a shorter distance for positioning than the time-based feature [37,39]. The proposed algorithm using the road shape spectrogram consists of three remedies: constructing a feature-indexed map; integrating the map-based positioning method and vehicle dynamics using the Kalman filter framework; and, using multiple maps generated using different conditionbased spectrograms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Inertial-Sensor-Based Non-Dead Reckoning Localization for Ground Vehicles

Gim

Ahn

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper proposes an inertial sensor-based positioning method without using dead reckoning. The basic concept estimates the current position based on a pre-indexed map composed of spectrograms of road shapes estimated using inertial sensor signals. The proposed positioning algorithm has three characteristics: a feature-indexed map, the Kalman filter for signal fusion, and the interacting multiple model to integrate multiple maps. The positioning algorithm shows higher positioning accuracy and shorter computing time than a simple scanning method. Furthermore, the size of the map does not proportionally increase as the total length of the road increases. Therefore, the proposed positioning algorithm can be used as a complementary or alternative positioning method to GPS-based positioning methods using only an inertial sensor that is cheap and reliable.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inertial-Sensor-Based Non-Dead Reckoning Localization for Ground Vehicles

Gim

Ahn

2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

CNVPS: Cooperative Neighboring Vehicle Positioning System Based on Vehicle-to-Vehicle Communication

Lee

Park

2019

IEEE Access

View full text Add to dashboard Cite

One of the key components of the intelligent transport system is to provide a safe driving environment. The ability to quickly and accurately recognize the surrounding environments and neighboring vehicles is essential for the development of safety-related services to provide a safe driving environment. Currently, research in this field is being conducted based on the global positioning system (GPS), light detection and ranging, camera, and ranging sensors. However, the currently used sensors cannot recognize a wide range of vehicles because of their limited range of recognition. Moreover, GPS-based studies are highly affected by the surrounding environment because of the nature of GPS and have relatively high error rates and low rate of location information updates. The use of GPS-based location information for safetyrelated services can result in negative consequences. In this paper, we propose a new positioning system, called cooperative neighboring vehicle positioning system (CNVPS). The CNVPS rapidly identifies the locations of neighboring vehicles based on their information obtained through various sensors and shares this information with a wide range of neighboring vehicles over vehicle-to-vehicle communications. The CNVPS also compensates the position of a neighboring vehicle by applying the maximum likelihood estimation to the duplicated position observation of the other neighboring vehicles. The simulation results show that the CNVPS achieves approximately 370% improvement in location errors over GPS, assuming that the rootmean-squared error of GPS is 15 m. In addition, the proposed system has a location refresh cycle ten times faster than the existing GPS-based system. INDEX TERMS Cooperative neighboring vehicle positioning system (CNVPS), intelligent systems, global positioning system.

show abstract

Ground vehicle localization based on inertial measurement signals

Cited by 2 publications

References 12 publications

Inertial-Sensor-Based Non-Dead Reckoning Localization for Ground Vehicles

Inertial-Sensor-Based Non-Dead Reckoning Localization for Ground Vehicles

CNVPS: Cooperative Neighboring Vehicle Positioning System Based on Vehicle-to-Vehicle Communication

Contact Info

Product

Resources

About