RobustStateNet: Robust ego vehicle state estimation for Autonomous Driving

Control of an ego vehicle for Autonomous Driving (AD) requires an accurate definition of its state. Implementation of various model-based Kalman Filtering (KF) techniques for state estimation is prevalent in the literature. These algorithms use measurements from IMU and input signals from steering and wheel encoders for motion prediction with physics-based models, and a Global Navigation Satellite System(GNSS) for global localization. Such methods are widely investigated and majorly focus on increasing the accuracy of the estimation. Ego motion prediction in these approaches does not model the sensor failure modes and assumes completely known dynamics with motion and measurement model noises. In this work, we propose a novel Recurrent Neural Network (RNN) based motion predictor that parallelly models the sensor measurement dynamics and selectively fuses the features to increase the robustness of prediction, in particular in scenarios where we witness sensor failures. This motion predictor is integrated into a KF-like framework, RobustStateNet that takes a global position from the GNSS sensor and updates the predicted state. We demonstrate that the proposed state estimation routine outperforms the Model-Based KF and KalmanNet architecture in terms of estimation accuracy and robustness. The proposed algorithms are validated in the modified NuScenes CAN bus dataset, designed to simulate various types of sensor failures

RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline

Loop Closure Detection (LCD) is an essential task in robotics and computer vision, serving as a fundamental component for various applications across diverse domains. These applications encompass object recognition, image retrieval, and video analysis. LCD consists in identifying whether a robot has returned to a previously visited location, referred to as a loop, and then estimating the related roto-translation with respect to the analyzed location. Despite the numerous advantages of radar sensors, such as their ability to operate under diverse weather conditions and provide a wider range of view compared to other commonly used sensors (e.g., cameras or LiDARs), integrating radar data remains an arduous task due to intrinsic noise and distortion. To address this challenge, this research introduces RadarLCD, a novel supervised deep learning pipeline specifically designed for Loop Closure Detection using the FMCW Radar (Frequency Modulated Continuous Wave) sensor. RadarLCD, a learning-based LCD methodology explicitly designed for radar systems, makes a significant contribution by leveraging the pre-trained HERO (Hybrid Estimation Radar Odometry) model. Being originally developed for radar odometry, HERO's features are used to select key points crucial for LCD tasks. The methodology undergoes evaluation across a variety of FMCW Radar dataset scenes, and it is compared to state-of-the-art systems such as Scan Context for Place Recognition and ICP for Loop Closure. The results demonstrate that RadarLCD surpasses the alternatives in multiple aspects of Loop Closure Detection.Comment: 7 pages, 2 figure

Advances in centerline estimation for autonomous lateral control

The ability of autonomous vehicles to maintain an accurate trajectory within their road lane is crucial for safe operation. This requires detecting the road lines and estimating the car relative pose within its lane. Lateral lines are usually retrieved from camera images. Still, most of the works on line detection are limited to image mask retrieval and do not provide a usable representation in world coordinates. What we propose in this paper is a complete perception pipeline based on monocular vision and able to retrieve all the information required by a vehicle lateral control system: road lines equation, centerline, vehicle heading and lateral displacement. We evaluate our system by acquiring data with accurate geometric ground truth. To act as a benchmark for further research, we make this new dataset publicly available at http://airlab.deib.polimi.it/datasets/.Comment: Presented at 2020 IEEE Intelligent Vehicles Symposium (IV), 8 pages, 8 figure

LiDAR point-cloud processing based on projection methods: a comparison

An accurate and rapid-response perception system is fundamental for autonomous vehicles to operate safely. 3D object detection methods handle point clouds given by LiDAR sensors to provide accurate depth and position information for each detection, together with its dimensions and classification. The information is then used to track vehicles and other obstacles in the surroundings of the autonomous vehicle, and also to feed control units that guarantee collision avoidance and motion planning. Nowadays, object detection systems can be divided into two main categories. The first ones are the geometric based, which retrieve the obstacles using geometric and morphological operations on the 3D points. The seconds are the deep learning-based, which process the 3D points, or an elaboration of the 3D point-cloud, with deep learning techniques to retrieve a set of obstacles. This paper presents a comparison between those two approaches, presenting one implementation of each class on a real autonomous vehicle. Accuracy of the estimates of the algorithms has been evaluated with experimental tests carried in the Monza ENI circuit. The position of the ego vehicle and the obstacle is given by GPS sensors with RTK correction, which guarantees an accurate ground truth for the comparison. Both algorithms have been implemented on ROS and run on a consumer laptop

Controlling the Flight of a Drone and Its Camera for 3D Reconstruction of Large Objects

In this paper we present a simple stand-alone system performing the autonomous acquisition of multiple pictures all around large objects, i.e., objects that are too big to be photographed from any side just with a camera held by hand. In this approach, a camera carried by a drone (an off-the-shelf quadcopter) is employed to carry out the acquisition of an image sequence representing a valid dataset for the 3D reconstruction of the captured scene. Both the drone flight and the choice of the viewpoints for shooting a picture are automatically controlled by the developed application, which runs on a tablet wirelessly connected to the drone, and controls the entire process in real time. The system and the acquisition workflow have been conceived with the aim to keep the user intervention minimal and as simple as possible, requiring no particular skill to the user. The system has been experimentally tested on several subjects of different shapes and sizes, showing the ability to follow the requested trajectory with good robustness against any flight perturbations. The collected images are provided to a scene reconstruction software, which generates a 3D model of the acquired subject. The quality of the obtained reconstructions, in terms of accuracy and richness of details, have proved the reliability and efficacy of the proposed system

Controlling the Flight of a Drone and Its Camera for 3D Reconstruction of Large Objects

In this paper we present a simple stand-alone system performing the autonomous acquisition of multiple pictures all around large objects, i.e., objects that are too big to be photographed from any side just with a camera held by hand. In this approach, a camera carried by a drone (an off-the-shelf quadcopter) is employed to carry out the acquisition of an image sequence representing a valid dataset for the 3D reconstruction of the captured scene. Both the drone flight and the choice of the viewpoints for shooting a picture are automatically controlled by the developed application, which runs on a tablet wirelessly connected to the drone, and controls the entire process in real time. The system and the acquisition workflow have been conceived with the aim to keep the user intervention minimal and as simple as possible, requiring no particular skill to the user. The system has been experimentally tested on several subjects of different shapes and sizes, showing the ability to follow the requested trajectory with good robustness against any flight perturbations. The collected images are provided to a scene reconstruction software, which generates a 3D model of the acquired subject. The quality of the obtained reconstructions, in terms of accuracy and richness of details, have proved the reliability and efficacy of the proposed system

Traffic lights detection and tracking for HD map creation

: HD-maps are one of the core components of the self-driving pipeline. Despite the effort of many companies to develop a completely independent vehicle, many state-of-the-art solutions rely on high-definition maps of the environment for localization and navigation. Nevertheless, the creation process of such maps can be complex and error-prone or expensive if performed via ad-hoc surveys. For this reason, robust automated solutions are required. One fundamental component of an high-definition map is traffic lights. In particular, traffic light detection has been a well-known problem in the autonomous driving field. Still, the focus has always been on the light state, not the features (i.e., shape, orientation, pictogram). This work presents a pipeline for lights HD-map creation designed to provide accurate georeferenced position and description of all traffic lights seen by a camera mounted on a surveying vehicle. Our algorithm considers consecutive detection of the same light and uses Kalman filtering techniques to provide each target's smoother and more precise position. Our pipeline has been validated for the detection and mapping task using the state-of-the-art dataset DriveU Traffic Light Dataset. The results show that our model is robust even with noisy GPS data. Moreover, for the detection task, we highlight how our model can correctly identify even far-away targets which are not labeled in the original dataset