Month: November 2024

ION Alberta In-person Meeting – Thursday, 28 November 2024

Topic: Alternatives to Kalman Filters

Speaker 1: Christian Phillips

Title: A Deep Learning Approach for the Classification of Multipath Ranging Errors in Challenging Urban Environments

Abstract: Distortion to the correlation function caused by multipath and non-line-of-sight signals can result in pseudorange errors on the order of several tens of meters in urban canyon environments. To address this problem, a deep learning approach for classifying multipath ranging error from a global navigation satellite systems (GNSS) receiver correlation function is presented. This approach uses a one-dimensional convolutional neural network, suitable for embedded applications, to classify the magnitude of pseudorange error associated with correlation functions. The network is trained and tested on live GNSS data collected in a challenging urban environment, and the capability of the model to remove high error measurements for a least-squares positioning solution is explored. The network has proven to be effective at detecting measurements with high multipath ranging error, and the removal of detected measurements reduced positioning error by up to 80%.

Bio: Christian Phillips is a graduate student in the Department of Geomatics Engineering at the University of Calgary. His research focuses on leveraging artificial intelligence to improve the performance of GNSS receivers in challenging operational environments. He is also a Software Developer at Hexagon’s Autonomy and Positioning division. He received his B.Sc. degree from the University of Manitoba in 2022.

Speaker 2: Ilyar Asl Sabbaghian Hokmabadi

Title: Computationally Efficient Particle Filtering for Fusing Angle of Arrival Beacons and IMU Measurements in Indoor Localization Applications

Abstract: In the recent past, beacons have emerged as a promising technology that meets the accuracy and reliability requirements of indoor localization. Due to the challenges regarding the loss of line-of-sight, indoor beacons often cannot provide a consistent performance throughout the navigation in standalone mode. Thus, the fusion of beacons with other sensors, such as inertial measurement units (IMU), has become an important topic for researchers. In recent decades, many estimation techniques have been proposed to achieve such sensor fusion. Among these, the Kalman filter family of estimators are ubiquitous due to their low computational cost. However, these classic estimators require an assumption of Gaussian distribution for the state variables (e.g., position, velocity, and attitude). Unfortunately, this simplistic assumption is not met in real-life scenarios. This research proposes an alternative approach based on particle filtering to fuse angle of arrival (AoA) beacon observations and inertial measurements. First, the theoretical background for reducing the dimensionality of state variables using AoA beacons is shown. This dimensionality reduction will contribute to reducing the computational cost of the particle filter. Second, it is shown that a low-cost and cm-level positioning can be achieved using only two beacons with the help of the proposed particle filter.

Bio: Ilyar Asl Sabbaghian Hokmabadi received his M.Sc. in Geomatics Engineering from the University of Calgary in 2018. Later, he received his Ph.D. degree from the University of Calgary in 2023. During his Ph.D., he developed many localization solutions using mobile and handheld systems. He has published and contributed to different areas, including indoor mapping using ultrasonic sensors, accurate 3D reconstruction using monocular cameras, and multisensory positioning solutions in indoor environments. Currently, Ilyar is an algorithm designer at Profound Positioning Inc., where his responsibilities include exploring state-of-the-art deep learning and advanced optimization methods to achieve a system-wide calibration of different types of sensors.

Location: Room 207 – Engineering Block G (ENG), University of Calgary Campus

Date: Thursday November 28, 2024

Time: Doors will open at 11:30am, presentation beginning at noon
Cost: $20 non-members, $18 members, $15 grad students, undergrad students $10, includes a light lunch and refreshments. All proceeds go towards two annual scholarships for students attending the University of Calgary

ION Alberta In-person Meeting – Thursday, 7 November 2024

We will have two speakers:

Meeting Theme: Smartphone Positioning

Rhea Zambra
Title: Smartphone HD Map Updates Using Monocular-Inertial ORB-SLAM3 and Gaussian Splatting

Abstract: Gaussian splatting has emerged as a state-of-the-art 3D representation technique due to its high-fidelity and fast rendering capabilities. While it has been successfully integrated into light detection and ranging (LiDAR) and depth-enabled simultaneous localization and mapping (SLAM) algorithms, its potential for accurate outdoor 3D mapping using smartphone data remains underexplored. Pre-built high definition (HD) maps are vital for autonomous vehicles but are costly to maintain, motivating research in decentralized, smartphone-enabled HD map update systems. Existing solutions lack direct 3D-to-3D point cloud comparison, which could offer more robust updates by bypassing segmentation-based object detection. In this paper, we present a novel post-processing pipeline that generates dense, accurate, and near-scale HD maps from smartphone data, enabling updates to existing LiDAR and multi-sensor generated base maps. Our approach uses monocular-inertial ORB-SLAM3 to recover a scaled camera trajectory, which uses loop-closure and keyframe selection to alleviate drift in the localization and point cloud reconstruction. The ORB-SLAM3 keyframes are then used to initialize a 3D Gaussian Splatting render of the scene, which densifies the point cloud using the images, and is then scaled by the monocular-inertial camera trajectory. The camera and IMU data are collected using an iPhone 14 Pro Max, at an outdoor loop at the University of Calgary that spans 158 meters. Both sensors are observed using the SensorLogger application, and the camera-IMU calibration is performed through Kalibr. This system results in a successful closed-loop 3D Gaussian render, producing a point cloud with 8.70% scale error and 0.493m root mean square (RMS) value for iterative closest point (ICP) when referenced to a LiDAR-IMU base map, showing the potential of smartphones in visual-inertial HD mapping. Additionally, the registration of a parked car demonstrates the system’s capability for accurate map updates when aligned with a LiDAR-based reference map.

Biography: Rhea Joyce Zambra is a M.Sc. student in the Department of Geomatics Engineering at the University of Calgary. She is part of the Intelligent Navigation and Mapping Lab, specializing in mapping and with research interest in the use of smartphones for accurate 3D reconstruction.

Naman Agarwal
Title: Application of Adaptive Kalman Filtering on Smartphone Positioning

Abstract: An Adaptive Kalman filter (AKF) is proposed which is used to estimate smartphone Global Navigation Satellite System (GNSS) pseudorange measurement variance. The filter is applied to stationary, bicycle and vehicle-based smartphone datasets collected in urban environments. The adaptive filter is compared to three other processing strategies: (i) conventional weighted least-squares, (ii) a velocity as random-walk Kalman filter (KF) for kinematic data or position as random-walk KF for static data, and (iii) an alternative KF implementation that uses Doppler to adapt process noise, all using a standard elevation and carrier-to-noise density ratio (C/N_0) measurement variance model. The adaptively estimated measurement variance is compared to the true error variance computed using the provided ground truth files and all four methods are evaluated in the position domain. The proposed AKF showed a horizontal positional accuracy improvement of 35.4%, 10.5%, and 27.3%, and a vertical positional accuracy improvement of 13.2%, 50.5%, and 59.6% for stationary, bicycle, and vehicle-based smartphone GNSS, respectively, compared to the second-best performing filter.

Biography: Naman Agarwal is a PhD student in the Department of Geomatics Engineering at the University of Calgary. He works in the PLAN lab under the supervision of Dr. Kyle O’Keefe. His main research area is “Precise Smartphone Positioning”.


Location: Room 207 – Engineering Block G (ENG), University of Calgary Campus

Date: Thursday November 7, 2024

Time: Doors will open at 11:30am, presentation beginning at noon
Cost: $20 non-members, $18 members, $15 grad students, undergrad students $10, includes a light lunch and refreshments. All proceeds go towards two annual scholarships for students attending the University of Calgary