LiDAR (Light detection and ranging) technology is an alternative to current manual methods of canopy geometry estimations in orange trees. The objective of this work was to compare different types of canopy volume estimations of orange trees, some inspired on manual methods and others based on a LiDAR sensor. A point cloud was generated for 25 individual trees using a laser scanning system. The convex-hull and the alpha-shape surface reconstruction algorithms were tested. LiDAR derived models are able to represent orange trees more accurately than traditional methods. However, results differ significantly from the current manual method. In addition, different 3D modeling algorithms resulted in different canopy volume estimations. Therefore, a new standard method should be developed and established.

This paper presents a concept of a laser scanner framework designed for obstacle avoidance used on mini fixed-wing unmanned aerial vehicles (UAVs) flying in outdoor environments. The innovation is a conical field of view that guarantees tri-dimensional (3D) obstacle detection and localization at any pitch or roll angle. This advantage is very important for the case of fixed-wing UAV flights where the attitude is changing rapidly. Measurement sequences create a map that is represented by a circular grid with the center fitted to the x-axis of the UAV's body, lying in the plane normal to the velocity vector and projected in the front of UAV. This means that the map cells contain differences between the safety zone radius and distances acquired from area in close proximity to the flight path. Actual UAV attitude can be compensated by rotation and shift of two masks of gains that are applied to the map to determine pitch and roll commands. Results of the simulation research conducted on the designed concept are very promising, as they present a combination of lateral and vertical obstacle avoidance. Based on the experience with laser rangefinders operating on a real UAV, it can be convincingly determined that the concept of the laser scanner is able to be brought into reality.

Three-dimensional (3D) data processing has applications in solving complex tasks such as object recognition, environment modeling, and robotic mapping and localization. Because using raw 3D data without preprocessing is very time-consuming, extraction of geometric features that describe the environment concisely is essential. In this sense, a plane can be a suitable geometric feature due to its simplicity of extraction and the abundance in indoor environments. This paper presents an online incremental plane extraction method using line segments for indoor environments. Our data collection system is based on a “nodding” laser scanner, so we exploit the incremental nature of its data acquisition in which physical rotation and 3D data processing are conducted in parallel. Line segments defined by two end points become supporting elements that comprise a plane, so a large proportion of scan points can be ignored once the line segments are extracted from each scan slice. This elimination of points reduces the algorithm complexity and computation time. Experiments with the tens of complete scan data sets which were acquired from a typical indoor environment demonstrated that our method was at least three times faster than the state-of-the-art methods.

Global localization of mobile robots has been well studied using the extended Kalman filter (EKF) method. This paper presents a fuzzy extended information filtering (FEIF) approach to improving global localization of an indoor autonomous mobile robot with ultrasonic and laser scanning measurements. A real-time FEIF algorithm is proposed to improve accuracy of static global pose estimation via multiple ultrasonic data. By fusing odometric, ultrasonic, and laser scanning data, a real-time FEIF-based pose tracking algorithm is developed to improve accuracy of the robot's continuous poses. Several experimental results are performed to confirm the efficacy of the proposed methods.