Autonomous navigation in urban environments faces significant challenges, particularly due to dynamic obstacles and occluded areas, such as those found at intersections. This paper presents a novel occlusion-aware risk assessment and navigation framework designed to address these issues. Unlike traditional methods, which often overlook risks in unobserved regions, the proposed approach leverages prior map data (e.g., OpenStreetMap) and real-time LiDAR sensor inputs to quantify risks from both visible and potential hidden obstacles. By combining static and dynamic information, the system enables robust trajectory planning using a tentacle-based approach, optimizing safety and task efficiency. Risk distributions are represented using particle-based modeling, offering simplicity and parallelizability. Evaluated in the Carla simulator across various scenarios, the framework demonstrates enhanced collision avoidance and optimal navigation, effectively balancing safety and speed in complex urban settings.

In a context of autonomous driving, perception of the surrounding is a crucial task. It characterizes the vehicle’s ability to simultaneously model its surroundings accurately and maintain its position in the environment. In this article, a new framework of mobile robot perception and risk assessment is proposed. Our approach aims to leverage the simultaneous combination of the standard occupancy grid map with a new map that we have called "knowledge map". This proposal was motivated by the fact that risk arises not only from obstacles but also from the lack of knowledge. Using this framework, we are able to assess the risk, mainly of collision, over a given path $\mathscr{P}$ and therefore compute an optimal navigation control of the robot. Thanks to the proposed Bayesian framework the paper also shows how we can combine both local measurements and existing map (eg. OpenStreetMap) and also take account of the robot’s localization errors.

In this paper, we address the problem of localizing mobile robots based on range-only measurements from low cost Ultra-Wide-Band (UWB) sensors. The proposed solution considers every static or mobile object as beacons with contextual information. A beacon-to-beacon measurement is performed using UWB sensors and the position estimation is computed by the target beacon. This strategy allows to hide the cooperative localization problem behind these measurements. The fusion algorithm is based on a Split Intersection Covariance Filter which allows to correctly handle the correlation between the poses estimations of the beacons. We present the consistency of this solution using a simulation with 3 robots and 4 static beacons and a real experimentation with 1 robot and 3 static beacons.