Learning complex navigation behaviors in simulation and successfully transferring them to the real world remains a significant challenge due to the sim-to-real gap. To address this issue, we propose a hybrid reconstruction approach that combines Light Detection and Ranging (LiDAR) data and monocular images to create visually and structurally realistic simulated environments. Specifically, we extract an accurate mesh from LiDAR measurements and integrate it with geometry-consistent Three-Dimensional Gaussian Splatting (3DGS) to render photorealistic images. Additionally, we present an end-to-end neural network designed to generate control commands for a mobile robot, and it is used to assess the effectiveness of the hybrid reconstructions in narrowing the sim-to-real gap. In the proposed model, we introduce a novel auxiliary branch and define loss functions to improve the network’s ability to learn safe and robust actions. The method, which is based on imitation learning, is evaluated by deploying it on a real robot to navigate over a total distance of 3.5 kilometers, and experimental results show that models trained purely in simulation achieve performance comparable to those trained on real-world data.

The tracking problem remains difficult because trackers may lose the target due to occlusions or changes in the target's appearance and start tracking another one, making it difficult to detect when they fail. To deal with these problems, we design a novel method to track objects robustly by fusing different trackers, using an Extended Kalman Filter, which helps to detect these situations and correct them. We use our tracker-fusion in a mobile robot to follow a pedestrian while also avoiding dynamic obstacles, an aspect not usually analyzed in pedestrian following. The perception of the environment is done with a 3D LiDAR and the tracking is performed on front-view images constructed from the point cloud. We show that by fusing two different trackers, the robot can follow the target during long experimental sessions where there are occlusions of the target by other people. The method is more precise and robust than using the trackers without the fusion.

Single Object Tracking methods are yet not robust enough because they may lose the target due to occlusions or changes in the target’s appearance, and it is difficult to detect automatically when they fail. To deal with these problems, we design a novel method to improve object tracking by fusing complementary types of trackers, taking advantage of each other’s strengths, with an Extended Kalman Filter to combine them in a probabilistic way. The environment perception is performed with a 3D LiDAR sensor, so we can track the object in the point cloud and also in the front-view image constructed from the point cloud. We use our tracker-fusion method in a mobile robot to follow pedestrians, also considering the dynamic obstacles in the environment to avoid them. We show that our method allows the robot to follow the target accurately during long experimental sessions where the trackers independently fail, demonstrating the robustness of our tracker-fusion strategy.

While complete localization approaches are widely studied in the literature, their data association and data representation subprocesses usually go unnoticed. However, both are a key part of the final pose estimation. In this work, we present DA-LMR (Delta-Angle Lane Marking Representation), a robust data representation in the context of localization approaches. We propose a representation of lane markings that encodes how a curve changes in each point and includes this information in an additional dimension, thus providing a more detailed geometric structure description of the data. We also propose DC-SAC (Distance-Compatible Sample Consensus), a data association method. This is a heuristic version of RANSAC that dramatically reduces the hypothesis space by distance compatibility restrictions. We compare the presented methods with some state-of-the-art data representation and data association approaches in different noisy scenarios. The DA-LMR and DC-SAC produce the most promising combination among those compared, reaching 98.1 % in precision and 99.7% in recall for noisy data with 0.5 m of standard deviation.

OpenStreetMaps (OSM) is currently studied as the environment representation for autonomous navigation. It provides advantages such as global consistency, a heavy-less map construction process, and a wide variety of road information publicly available. However, the location of this information is usually not very accurate locally. In this paper, we present a complete autonomous navigation pipeline using OSM information as environment representation for global planning. To avoid the flaw of local low-accuracy, we offer the novel LiDAR-based Naive-Valley-Path (NVP) method that exploits the concept of “valley” areas to infer the local path always furthest from obstacles. This behavior allows navigation always through the center of trafficable areas following the road’s shape independently of OSM error. Furthermore, NVP is a naive method that is highly sample-time-efficient. This time efficiency also enables obstacle avoidance, even for dynamic objects. We demonstrate the system’s robustness in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km with 0.24 meters of average error against the road’s center with a 19.8 ms of average sample time. Our vehicle avoids static obstacles in the road and even dynamic ones, such as vehicles and pedestrians.

Nowadays, robotics education programs are used to promote the skills of students in STEM content at different levels of education for a wide variety of studies, not just engineering. This paper describes an educational program of robotics, applied precisely to the laboratory sessions of a robotics initiation course taught in the first year of a degree in Robotic Engineering, as well as the results of the application of that program in different academic years. The educational program contemplates that the students, grouped in work teams, have to design, assemble, and program a small mobile robot, destined to a final competition in which all the teams participate.