ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM

Real-time six degrees-of-freedom pose estimation with ground vehicles represents a relevant and well-studied topic in robotics due to its many applications such as autonomous driving and 3D mapping. Although some systems already exist, they are either not accurate or they struggle in real-time settings. In this letter, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking, possibly aided by a pre-tracking module, and floor detection, to ground optimize the estimated trajectory. Efficient multi-steps loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud-based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR, LIO-SAM and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI (urban driving) and Chilean (underground mine) datasets

Advancements in Radar Odometry

Radar odometry estimation has emerged as a critical technique in the field of autonomous navigation, providing robust and reliable motion estimation under various environmental conditions. Despite its potential, the complex nature of radar signals and the inherent challenges associated with processing these signals have limited the widespread adoption of this technology. This paper aims to address these challenges by proposing novel improvements to an existing method for radar odometry estimation, designed to enhance accuracy and reliability in diverse scenarios. Our pipeline consists of filtering, motion compensation, oriented surface points computation, smoothing, one-to-many radar scan registration, and pose refinement. The developed method enforces local understanding of the scene, by adding additional information through smoothing techniques, and alignment of consecutive scans, as a refinement posterior to the one-to-many registration. We present an in-depth investigation of the contribution of each improvement to the localization accuracy, and we benchmark our system on the sequences of the main datasets for radar understanding, i.e., the Oxford Radar RobotCar, MulRan, and Boreas datasets. The proposed pipeline is able to achieve superior results, on all scenarios considered and under harsh environmental constraints

On the precision of 6 DoF IMU-LiDAR based localization in GNSS-denied scenarios

Positioning and navigation represent relevant topics in the field of robotics, due to their multiple applications in real-world scenarios, ranging from autonomous driving to harsh environment exploration. Despite localization in outdoor environments is generally achieved using a Global Navigation Satellite System (GNSS) receiver, global navigation satellite system-denied environments are typical of many situations, especially in indoor settings. Autonomous robots are commonly equipped with multiple sensors, including laser rangefinders, IMUs, and odometers, which can be used for mapping and localization, overcoming the need for global navigation satellite system data. In literature, almost no information can be found on the positioning accuracy and precision of 6 Degrees of Freedom Light Detection and Ranging (LiDAR) localization systems, especially for real-world scenarios. In this paper, we present a short review of state-of-the-art light detection and ranging localization methods in global navigation satellite system-denied environments, highlighting their advantages and disadvantages. Then, we evaluate two state-of-the-art Simultaneous Localization and Mapping (SLAM) systems able to also perform localization, one of which implemented by us. We benchmark these two algorithms on manually collected dataset, with the goal of providing an insight into their attainable precision in real-world scenarios. In particular, we present two experimental campaigns, one indoor and one outdoor, to measure the precision of these algorithms. After creating a map for each of the two environments, using the simultaneous localization and mapping part of the systems, we compute a custom localization error for multiple, different trajectories. Results show that the two algorithms are comparable in terms of precision, having a similar mean translation and rotation errors of about 0.01 m and 0.6 degrees, respectively. Nevertheless, the system implemented by us has the advantage of being modular, customizable and able to achieve real-time performance

IC3D: Image-Conditioned 3D Diffusion for Shape Generation

In the last years, Denoising Diffusion Probabilistic Models (DDPMs) obtained state-of-the-art results in many generative tasks, outperforming GANs and other classes of generative models. In particular, they reached impressive results in various image generation sub-tasks, among which conditional generation tasks such as text-guided image synthesis. Given the success of DDPMs in 2D generation, they have more recently been applied to 3D shape generation, outperforming previous approaches and reaching state-of-the-art results. However, 3D data pose additional challenges, such as the choice of the 3D representation, which impacts design choices and model efficiency. While reaching state-of-the-art results in generation quality, existing 3D DDPM works make little or no use of guidance, mainly being unconditional or class-conditional. In this paper, we present IC3D, the first Image-Conditioned 3D Diffusion model that generates 3D shapes by image guidance. It is also the first 3D DDPM model that adopts voxels as a 3D representation. To guide our DDPM, we present and leverage CISP (Contrastive Image-Shape Pre-training), a model jointly embedding images and shapes by contrastive pre-training, inspired by text-to-image DDPM works. Our generative diffusion model outperforms the state-of-the-art in 3D generation quality and diversity. Furthermore, we show that our generated shapes are preferred by human evaluators to a SoTA single-view 3D reconstruction model in terms of quality and coherence to the query image by running a side-by-side human evaluation

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