Change Detection of Urban Trees in MLS Point Clouds Using Occupancy Grids

Trees play an important role in the complex system of urban environments. Their benefits to environment and health are manifold. Yet, especially near streets, the traffic can be impaired by a limited clearance. Even injuries could be caused by breaking tree parts. Hence, it is important to capture the trees in the frame of a tree cadastre and to ensure regular monitoring. Mobile laser scanning (MLS) can be used for data acquisition, followed by an automated analysis of the point clouds acquired over time. The presented approach uses occupancy grids with a grid size of 10 cm, which enable the comparison of several epochs in three-dimensional space. Prior to that, a segmentation of single tree objects is conducted. After cylinder-based trunk localisation, closely neighboured tree crowns are separated using weights derived from local point densities. Therefore, changes for every single tree can be derived with regard to its parameters and its point cloud. The testing area is set along an urban street in Munich, Germany, using the publicly available benchmark data sets TUM-MLS-2016/2018. In the frame of the evaluation, tree objects are geo-referenced and mapped in 2D. The tree parameters height and diameter at breast height are derived. The geometric evaluation of the change analysis facilitates not only the acquisition of stock changes, but also the detection of shape changes for the tree objects.

Probability of Collision between Autonomous Mobile Robot with an Obstacle

The probability estimation problem of a collision between path tracking for an autonomous mobile robot with an obstacle is considered. We reviewed and analyzed methods for solving this problem. We show that reviewed methods use periodically updated grid maps (occupancy grids). The new method of probability estimation of the collision between the mobile robot with an obstacle is presented. This method based on the use of probabilistic grid map. Each cell of this map stores the estimated probability that the obstacle is located within. In addition, this map stores the conditional probability of occupying of the map cells by a robot, taking into account the possible lateral and angular deviation from the planned trajectory. This deviation caused by error connected with dynamic characteristics of the tracking system. To build the probabilistic occupancy grid, the dynamically updated multilayer grid map was used. Each layer of this map, except for the resulting output, has been filled with the data obtained from classifiers which process information incoming from sensory of the robot. This layer is the result of Bayesian inference from the layers laying below. The motion control system provides construction of the multilayered grid maps, probabilistic occupancy grids, coordinate estimations, path planning, motion tracking and the probability estimation for collision with obstacles. The method such estimation is implemented as an embedded module compatible with ROS (Robot Operating System). The description of experiments with the mobile robot in-nature (on the field) is given in the case when a motile obstacle appears intercepting the planned path. The estimated changes of probability for a collision between the mobile robot with obstacle are presented, interpretation of the obtained results is also given. Here we demonstrated the necessity of collision probability estimation for assessment of the risk as the main safety indicator of the given motion control system. Results of this work are considered and evaluated as a solution to the problem of ensuring the safety of motion tracking for autonomous mobile robots.

A Fuzzy Gain-Based Dynamic Ant Colony Optimization for Path Planning in Dynamic Environments

Path planning can be perceived as a combination of searching and executing the optimal path between the start and destination locations. Deliberative planning capabilities are essential for the motion of autonomous unmanned vehicles in real-world scenarios. There is a challenge in handling the uncertainty concerning the obstacles in a dynamic scenario, thus requiring an intelligent, robust algorithm, with the minimum computational overhead. In this work, a fuzzy gain-based dynamic ant colony optimization (FGDACO) for dynamic path planning is proposed to effectively plan collision-free and smooth paths, with feasible path length and the minimum time. The ant colony system’s pheromone update mechanism was enhanced with a sigmoid gain function for effective exploitation during path planning. Collision avoidance was achieved through the proposed fuzzy logic control. The results were validated using occupancy grids of variable size, and the results were compared against existing methods concerning performance metrics, namely, time and length. The consistency of the algorithm was also analyzed, and the results were statistically verified.

Developing Occupancy Grid with Automotive Simulation Environment

This study presents the process employed in prototyping and early evaluation of automotive perception algorithms. The data generation was performed using an automotive virtual validation tool. The off-the-shelf simulation framework used was expanded to include phenomenological sensors model that allowed for a simplified simulation of radars, lidars, and cameras. This paper extends the description of the methods for the generation of control algorithms. The work presented also includes a description of relevant data fusion methods for building occupancy grids. Results were obtained by performing a comparison of algorithm results against ground-truth. This virtual validation was used to enable early definition and verification of system-level requirements, narrow down performance assessment methods, and identify performance limitations before data from real sensors are available.