Corso, Nicholas Giovanni (University of California, Berkeley), “Sensor Fusion and Online Calibration of an Ambulatory Backpack System for Indoor Mobile Mapping” (2016)

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

News and Resources for Members of the IEEE Signal Processing Society

Corso, Nicholas Giovanni (University of California, Berkeley), “Sensor Fusion and Online Calibration of an Ambulatory Backpack System for Indoor Mobile Mapping” (2016)

Corso, Nicholas Giovanni (University of California, Berkeley), “Sensor Fusion and Online Calibration of an Ambulatory Backpack System for Indoor Mobile Mapping” (2016), advisor: Zakhor, Avideh

GPS-denied indoor mobile mapping has been an active area of research for many years. With applications such as historical preservation, entertainment, and augmented reality, the demand for both fast and accurate scanning technologies has dramatically increased. In this thesis, they present two algorithmic pipelines for GPS-denied indoor mobile 3D mapping using an ambulatory backpack system. By mounting scanning equipment on a backpack system, a human operator can traverse the interior of a building to produce a high-quality 3D reconstruction. In each of their presented algorithmic pipelines, data from a number of 2D laser scanners, a camera, and an IMU is fused together to track the 3D position of the system as the operator traverses an unknown environment.

This thesis presents a number of novel contributions for indoor GPS-denied 2.5 and 3D mobile mapping using a number of 2D laser scanners, a camera, and an IMU. First, for 3D mapping they develop a tightly coupled EKF estimator for fusing data from all sensors into a single optimized 3D trajectory. By formulating each sensor's contributions independently, they demonstrate a modular algorithm that easily scales to an arbitrary number of 2D laser scanners. In contrast to existing work that either assumes a known fixed map or limits the environment to a set of axis aligned planes, they demonstrate the ability to map environments containing horizontal and vertical planes of arbitrary orientation with no a priori information. Additionally, through timing and complexity analysis, they demonstrate that the runtime of the proposed EKF estimator is only linear in the acquisition time. Secondly, by including in their EKF estimator the laser scanner's spatial and temporal calibration parameters, they present a novel laser calibration methodology. Through simulated and real-world data, they validate that the proposed algorithms are capable of calibrating both the extrinsic and temporal misalignments present in their system's laser data. Lastly, they address the scalability of the proposed approach by utilizing a graph optimization post processing step that overcomes any accumulated drift in the EKF estimator. They then validate the proposed 3D end-to-end localization system using 3 multi-story datasets collected from real-world environments. The system's reconstructions are compared against CAD drawings of the buildings and are shown to achieve an intersection over union of over 96% on all datasets. Lastly, they demonstrate accuracy improvements over their 2.5D methods using a comparison test against data collected with a static scanner.

In addition to 3D mapping, they also present a methodology for 2.5D mapping with three novel contributions. First, they present a method for automatically segmenting barometric pressure data based on the floor of the building it was collected from. Specifically, by using Bayesian non-parametrics they are able to demonstrate simultaneous floor detection and the corresponding data segmentation. The data segmentation is then used to extend classical 2D particle filtering across any number of discrete building stories. Secondly, they demonstrate a genetic scan matching algorithm used to estimate loop closure constraints even without an accurate initial condition. Through simulation and real-world experiments they show an improvement over state of the art scan matching techniques. Next, they present two metrics that are used to validate the results of the genetic scan matching algorithm. They use both a correlation and shape metric to demonstrate robust and accurate validation of loop closure constraints in indoor environments. Lastly, they compare and characterize the performance of the proposed 3D and 2.5D mapping techniques developed in this thesis. Although the 2.5D mapping techniques are more computationally lightweight, they show that the accuracy of system is significantly improved using the 3D mapping algorithm.

Table of Contents:

SPS on Twitter

  • DEADLINE EXTENDED: The 2023 IEEE International Workshop on Machine Learning for Signal Processing is now accepting…
  • ONE MONTH OUT! We are celebrating the inaugural SPS Day on 2 June, honoring the date the Society was established in…
  • The new SPS Scholarship Program welcomes applications from students interested in pursuing signal processing educat…
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now seeking submissions for a Special…
  • Test your knowledge of signal processing history with our April trivia! Our 75th anniversary celebration continues:…

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel