On improving low-cost IMU performance for online trajectory estimation

Yudanto, Risang Gatot; Ompusunggu, Agusmian Partogi; Bey-Temsamani, Abdellatif

doi:10.1117/12.2180878

Cited by 4 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However the position measurements are drifting over time due to integration of noisy IMUs signals, especially when a low-cost Micro Electro-Mechanical Systems (MEMS) based INS is used. Experimental results from our study on using only low-cost IMU for online trajectory estimation is presented in [10].…”

Section: B Experimental Results From Existing Systemsmentioning

confidence: 99%

“…A conversion from tangential-normal (t-n) into x-y directions is needed and as illustrated in the AGV scheme in Figure 1, it can be expressed in (10) and (11): (10) θ θ cos sin ⋅ + ⋅ = n t y a a a (11) The same concept can also be applied to define relationship between linear velocities in x-y with velocities in t-n directions.…”

Section: Sensor Fusion Designmentioning

confidence: 99%

See 1 more Smart Citation

Sensor fusion for indoor navigation and tracking of automated guided vehicles

Yudanto

Petré

2015

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

A novel sensor fusion approach between a wireless Ultra-Wideband (UWB) Indoor Positioning System (IPS) and an Inertial Navigation System (INS) is presented for real-time indoor navigation and tracking of Automated Guided Vehicles (AGVs) and Mobile Robots in factories and warehouses. AGV applications are particularly challenging since they require very low position errors and very high position update rates to be able to track the highly dynamic movements in real-time. The sensor fusion algorithm consists of two main blocks: the first is a delay compensation block that compensates the delayed results of position estimation coming from the IPS, while the second block is a multi-rate Extended Kalman Filter (EKF) that combines dynamic models of the movements, delay-compensated position measurements from the IPS and measurements coming from the INS. To validate the proposed sensor fusion approach, extensive tests have been performed on a real-time platform using a combination of an Ubisense UWB IPS and an INS based on two low-cost MEMS accelerometers and one gyroscope mounted on the Flanders Make Badminton Robot performing highly dynamic movements.The results show that the proposed sensor fusion approach is able to deliver a 1 kHz position update rate in realtime operation with a position error standard deviation of 3.7 cm for linear movements and 1.7 degrees for rotational movements. These are significant performance improvements compared to a stand-alone Ubisense UWB indoor positioning system or a standalone inertial navigation system.

show abstract

Section: B Experimental Results From Existing Systemsmentioning

confidence: 99%

Section: Sensor Fusion Designmentioning

confidence: 99%

Sensor fusion for indoor navigation and tracking of automated guided vehicles

Yudanto

Petré

2015

2015 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

show abstract

“…Therefore, none of these solutions offers a reliable method of high accuracy for position and attitude estimation, as is needed in large grapevines or fruit plantations, where the rows are narrow and space is limited for maneuvers without the use of expensive sensors [21][22][23].The accuracy of consumer-grade IMUs is still limited because the estimated position and heading often have large cumulative errors over time, while they cannot self-determine the vehicle's initial state. Complex sensor calibration procedures are also required to improve the estimation of the heading angle [24] by reducing sensor drift [25]. The use of real-time kinematic (RTK) GPS is another method to improve the accuracy of the GPS navigation system based on differential calculations that can determine the position of a vehicle with centimeter-level accuracy.…”

Section: Introductionmentioning

confidence: 99%

RoboNav: An Affordable Yet Highly Accurate Navigation System for Autonomous Agricultural Robots

2022

View full text Add to dashboard Cite

The paper presents RoboNav, a cost-effective and accurate decimeter-grade navigation system that can be used for deployment in the field of autonomous agricultural robots. The novelty of the system is the reliance on a dual GPS configuration based on two u-blox modules that work in conjunction with three low-cost inertial sensors within a Gaussian Sum Filter able to combine multiple Extended Kalman filters dealing with IMU bias and GPS signal loss. The system provides estimation of both position and heading with high precision and robustness, at a significantly lower cost than existing equivalent navigation systems. RoboNav is validated in a commercial vineyard by performing experimental tests using an all-terrain tracked robot commanded to follow a series of GPS waypoints, trying to minimize the crosstrack error and showing average errors on the order of 0.2 m and 0.2∘ for the measurement of position and yaw angle, respectively.

show abstract

“…First, antennas are only sparsely textured and usually have simple shapes with smooth surfaces (see Figure 1 a), providing a few of the distinctive features and stable matches (see Figure 1 b,c) that are usually required for existing feature-based pose estimation methods. Second, the IMU sensors in mobile phones are usually ultra-low-cost (consumer-level) microelectromechanical system (MEMS) sensors with poor accuracy [ 16 , 17 , 18 ]; however, accuracy is of key importance for industry applications, such as network optimization [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones

Wang

Jin

Wang

2017

Sensors

View full text Add to dashboard Cite

The poses of base station antennas play an important role in cellular network optimization. Existing methods of pose estimation are based on physical measurements performed either by tower climbers or using additional sensors attached to antennas. In this paper, we present a novel non-contact method of antenna pose measurement based on multi-view images of the antenna and inertial measurement unit (IMU) data captured by a mobile phone. Given a known 3D model of the antenna, we first estimate the antenna pose relative to the phone camera from the multi-view images and then employ the corresponding IMU data to transform the pose from the camera coordinate frame into the Earth coordinate frame. To enhance the resulting accuracy, we improve existing camera-IMU calibration models by introducing additional degrees of freedom between the IMU sensors and defining a new error metric based on both the downtilt and azimuth angles, instead of a unified rotational error metric, to refine the calibration. In comparison with existing camera-IMU calibration methods, our method achieves an improvement in azimuth accuracy of approximately 1.0 degree on average while maintaining the same level of downtilt accuracy. For the pose estimation in the camera coordinate frame, we propose an automatic method of initializing the optimization solver and generating bounding constraints on the resulting pose to achieve better accuracy. With this initialization, state-of-the-art visual pose estimation methods yield satisfactory results in more than 75% of cases when plugged into our pipeline, and our solution, which takes advantage of the constraints, achieves even lower estimation errors on the downtilt and azimuth angles, both on average (0.13 and 0.3 degrees lower, respectively) and in the worst case (0.15 and 7.3 degrees lower, respectively), according to an evaluation conducted on a dataset consisting of 65 groups of data. We show that both of our enhancements contribute to the performance improvement offered by the proposed estimation pipeline, which achieves downtilt and azimuth accuracies of respectively 0.47 and 5.6 degrees on average and 1.38 and 12.0 degrees in the worst case, thereby satisfying the accuracy requirements for network optimization in the telecommunication industry.

show abstract

On improving low-cost IMU performance for online trajectory estimation

Cited by 4 publications

References 4 publications

Sensor fusion for indoor navigation and tracking of automated guided vehicles

Sensor fusion for indoor navigation and tracking of automated guided vehicles

RoboNav: An Affordable Yet Highly Accurate Navigation System for Autonomous Agricultural Robots

Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones

Contact Info

Product

Resources

About