Week 3 (8/3 – 14/3) – Simulation tools

One of the goals of this work is to complement the simulation tools already created to contemplate the study of negative obstacles and inverted road curbs.

Simulation tools are very important to visualize and understand the problem before field analysis and test different algorithms.

In the line of this work there are two simulators in progress: a Matlab simulator that is based of mathematical equations to simulate laser beams and its intersections with a defined plane, and a Gazebo simulator that has a 3D model of the AtlasCar2 and has features that allows the definition of different types of LIDAR and also 3D models to simulate different types of roads. The last one is implemented in ROS and has the ultimate goal of simulating the movement of the car along the road and create a point cloud as if the real world car was actually moving.

Gazebo simulator
Matlab simulator

Both this tools are still in progress with the final goal of simulating different types of situations in different road conditions.

Week 2 (1/3 – 7/3) – The understanding of previous work

After the study of different works and approaches to road detection using LIDAR, it was time to understand the work already done in the AtlasCar2 in previous years.

In the following figure can be seen a play of a rosbag that recorded a trip around University of Aveiro. The white points in the image represent the raw point cloud obtained directly from the LIDAR readings, and the colored points represent a point cloud where the algorithm filters are already applied.

This algorithm consists on the application of a voxel filter to decrease the point cloud density, followed by the elimination of points that have few neighbors in a predefined radius, in this case, less than 15 neighbors in a 0.2m radius. This allows to identify the zones with higher density that correspond to well defined perpendicular road curbs.

Where this algorithm fails is when there are negative obstacles that form a shadow and represent a zone where there is no points in the point cloud.

It is then concluded that the elimination of points from the raw point cloud is not the best way to go, or at least if not combined with the parallel use of other algorithms that analyze not only the high density zones as well as the low or no density zones.

The Journey Begins

In today’s world there is a constant search for speed, autonomy and efficiency. People constantly search for solutions and technologies that can upgrade their lifestyle and save them time. In that line, the development autonomous vehicles are growing at high speed and are one of the most talked topics of the present.

One of the main fields of autonomous driving is the perception of road and to determine the navigable limits of the road, that is, the limits where it is safe for the vehicle to move. Therefore, arises the theme of this dissertation that aims to find a solution for that problem with a use of a LIDAR sensor mounted on the front of a car close to the ground. LIDAR sensors measure the distance to the closest object and with them it is possible to withdraw a point cloud with three dimensional points corresponding to the intersection of laser beams with the objects ahead.

There are three main goals to be achieved in the end of this dissertation. The first one is to parameterize and improve the existing solution of road curb detection and obstacles at road level. The second one is to study and develop a solution for low curbs with accumulated point clouds in LIDAR. And the last one and to test and integrate the developed solution in the real environment aboard AtlasCar2.

This dissertation is one among many done in the AtlasCar2 and pretends to complement and improve the previous work. The majority of the work will be done aboard the car and in LAR (Laboratory of Automation and Robotics) and will be oriented by professor Vitor Santos.