UC Berkeley

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, we 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 our 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 we 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, we 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, we 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, we demonstrate that the runtime of the proposed EKF estimator is only linear in the acquisition time. Secondly, by including in our EKF estimator the laser scanner's spatial and temporal calibration parameters, we present a novel laser calibration methodology. Through simulated and real-world data, we validate that the proposed algorithms are capable of calibrating both the extrinsic and temporal misalignments present in our system's laser data. Lastly, we address the scalability of the proposed approach by utilizing a graph optimization post processing step that overcomes any accumulated drift in the EKF estimator. We 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, we demonstrate accuracy improvements over our 2.5D methods using a comparison test against data collected with a static scanner.

In addition to 3D mapping, we also present a methodology for 2.5D mapping with three novel contributions. First, we 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 we 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, we demonstrate a genetic scan matching algorithm used to estimate loop closure constraints even without an accurate initial condition. Through simulation and real-world experiments we show an improvement over state of the art scan matching techniques. Next, we present two metrics that are used to validate the results of the genetic scan matching algorithm. We use both a correlation and shape metric to demonstrate robust and accurate validation of loop closure constraints in indoor environments. Lastly, we 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, we show that the accuracy of system is significantly improved using the 3D mapping algorithm.