Baoxin Hu

Improving the efficiency and accuracy of individual tree crown delineation from high-density LiDAR data

Abstract Canopy height model (CHM) derived from LiDAR (Light Detection And Ranging) data has been commonly used to generate segments of individual tree crowns for forest inventory and sustainable management. However, branches, tree crowns, and tree clusters usually have similar shapes and overlapping sizes, which cause current individual tree crown delineation methods to work less effectively on closed canopy, deciduous or mixedwood forests. In addition, the potential of 3-dimentional (3-D) LiDAR data is not fully realized by CHM-oriented methods. In this study, a framework was proposed to take advantage of the simplicity of a CHM-oriented method, detailed vertical structures of tree crowns represented in high-density LiDAR data, and any prior knowledge of tree crowns. The efficiency and accuracy of ITC delineation can be improved. This framework consists of five steps: (1) determination of dominant crown sizes; (2) generation of initial tree segments using a multi-scale segmentation method; (3) identification of “problematic” segments; (4) determination of the number of trees based on the 3-D LiDAR points in each of the identified segments; and (5) refinement of the “problematic” segments by splitting and merging operations. The proposed framework was efficient, since the detailed examination of 3-D LiDAR points was not applied to all initial segments, but only to those needed further evaluations based on prior knowledge. It was also demonstrated to be effective based on an experiment on natural forests in Ontario, Canada. The proposed framework and specific methods yielded crown maps having a good consistency with manual and visual interpretation. The automated method correctly delineated about 74% and 72% of the tree crowns in two plots with mixedwood and deciduous trees, respectively.

Low Cost Multisensor Kinematic Positioning and Navigation System with Linux/RTAI

Despite its popularity, the development of an embedded real-time multisensor kinematic positioning and navigation system discourages many researchers and developers due to its complicated hardware environment setup and time consuming device driver development. To address these issues, this paper proposed a multisensor kinematic positioning and navigation system built on Linux with Real Time Application Interface (RTAI), which can be constructed in a fast and economical manner upon popular hardware platforms. The authors designed, developed, evaluated and validated the application of Linux/RTAI in the proposed system for the integration of the low cost MEMS IMU and OEM GPS sensors. The developed system with Linux/RTAI as the core of a direct geo-referencing system provides not only an excellent hard real-time performance but also the conveniences for sensor hardware integration and real-time software development. A software framework is proposed in this paper for a universal kinematic positioning and navigation system with loosely-coupled integration architecture. In addition, general strategies of sensor time synchronization in a multisensor system are also discussed. The success of the loosely-coupled GPS-aided inertial navigation Kalman filter is represented via post-processed solutions from road tests.

Comprehensive Utilization of Temporal and Spatial Domain Outlier Detection Methods for Mobile Terrestrial LiDAR Data

Terrestrial LiDAR provides many disciplines with an effective and efficient means of producing realistic three-dimensional models of real world objects. With the advent of mobile terrestrial LiDAR, this ability has been expanded to include the rapid collection of three-dimensional models of large urban scenes. For all its usefulness, it does have drawbacks. One of the major problems faced by the LiDAR industry today is the automatic removal of outlying data points from LiDAR point clouds. This paper discusses the development and combined implementation of two methods of performing outlier detection in georeferenced point clouds. These methods made use of the raw data available from most time-of-flight mobile terrestrial LiDAR scanners in both the temporal and spatial domains. The first method involved a moving fixed interval smoother derived from the well-known position velocity acceleration Kalman Filter. The second method fitted a quadratic curved surface to sections of LiDAR data. The combined use of these routines is discussed through examples with real LiDAR data.

Multi-frame Visual Odometry in Image-Aided Inertial Navigation System

This paper presents a novel stereo image-based image aided inertial navigation algorithm for reducing position and orientation drifts during GNSS outages or in a poor GNSS environment. Usually, the image aided navigation based on the visual odometry uses the tracked features only from a pair of the consecutive image frames. The proposed method integrates the features tracked from all overlapping image frames towards accuracy improvement and drift reduction. The measurement equation system in this multi-frame visual odometry algorithm (MFVO) is derived from Simultaneous Localization and Mapping (SLAM) measurement equation system where the landmark position parameters in SLAM are algebraically eliminated by time-differencing the measurement at two consecutive epochs. However the resulted time-differenced measurements are time-correlated. Through a sequential de-correlation the Kalman filter measurement update can be performed sequentially and optimally. Monte Carlo simulations show that the MFVO and SLAM pose estimates are similar. Compared with SLAM, the proposed method uses less computation resources especially when the number of features in view is large. The results from a real dataset are also presented.

An Unconventional Full Tightly-Coupled Multi-Sensor Integration for Kinematic Positioning and Navigation

Conventionally, all of the sensors, except the IMUs, function as aiding sensors in the multisensor integrated kinematic positioning and navigation. In this way, the IMU measurements are only used in free inertial navigation calculation, not through measurement update in Kalman filter (KF) between two adjacent aiding measurement epochs. This paper strives for a novel structure of IMU/GNSS integration KF, which deploys a kinematic trajectory model as the core of the KF system model and utilizes all of the measurements, inclusive of the ones from IMUs, through measurement updates. This novel multisensor integration strategy takes advantage of modern computing technology and well advances the realization of Kaman filter for a better utilization of the IMU measurements, especially either with low-cost IMUs or in poor GNSS and/or GNSS denied environment. Moreover, one no longer needs to distinguish between the core sensors and the aiding sensors. The conceptual comparison with the conventional error-state and error measurement based inertial navigation integration shows its advantages. The results from real road tests along with discussions are also presented.

An Algorithm for Boundary Adjustment toward Multi-Scale Adaptive Segmentation of Remotely Sensed Imagery

A critical step in object-oriented geospatial analysis (OBIA) is image segmentation. Segments determined from a lower-spatial resolution image can be used as the context to analyse a corresponding image at a higher-spatial resolution. Due to inherent differences in perceptions of a scene at different spatial resolutions and co-registration, segment boundaries from the low spatial resolution image need to be adjusted before being applied to the high-spatial resolution image. This is a non-trivial task due to considerations such as noise, image complexity, and determining appropriate boundaries, etc. An innovative method was developed in the study to solve this. Adjustments were executed for each boundary pixel based on the minimization of an energy function characterizing local homogeneity. It executed adjustments based on a structure which rewarded movement towards edges, and superior changes towards homogeneity. The developed method was tested on a set of Quickbird, ASTER and a lower resolution, resampled, Quickbird image, over a study area in Ontario, Canada. Results showed that the adjusted-segment boundaries obtained from the lower resolution imagery aligned well with the features in the Quickbird imagery.

Loosely coupled visual odometry aided inertial navigation system using discrete extended Kalman filter with pairwise time correlated measurements

This paper presents an algorithm for processing pairwise time-correlated measurements in a Kalman filter where the measurement vector at an epoch is correlated only with the measurement vector at the epoch before. Time-correlated errors are usually modelled by a shaping filter, which is here realized using Cholesky factors as coefficients derived from the variance and covariance matrices of the measurement noise vectors. Results with the simulated data show that the proposed approach performs better than the existing ones and provides more realistic covariance estimates. Furthermore, the proposed algorithm was applied to visual odometry aided-INS and the results show an improvement of 7% in the position drifts in comparison with the conventional shaping filter.