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4D imaging radars, commonly known as 4D radars, deliver comprehensive point cloud data that encapsulates range, azimuth, elevation, and Doppler velocity information even in harsh environmental conditions, such as
rain, snow, smoke, and fog. However, 4D radar data also suffers from high noise and sparsity, which poses great challenges for SLAM applications. This paper presents RIV-SLAM, a complete radar-inertial-velocity optimization-based graph SLAM system designed to exploit the full potential of 4D imaging radar technology. RIV-SLAM consists of four integral components: front-end, loop closure, IMU pre-integration and
graph optimization, each optimized to effectively leverage the unique attributes of radar data and tightly coupled with IMU data. This is also the first SLAM system known to us that outputs an optimized ego velocity. This capability ensures reliable ego motion estimation under extreme conditions (e.g., wheel odometry fails). Furthermore, we develop a new
ground extraction approach, specifically adapted for the 4D imaging radar, which substantially improves the system’s z-axis accuracy. Comprehensive evaluations of the RIV-SLAM system on a variety of datasets demonstrate its superior performance, significantly surpassing existing state-of-the-art Radar-SLAM frameworks. The code of RIV-SLAM will be released at: RIV-
SLAM
Infradar-Localization: single-chip infrared- and radar-based Monte Carlo localization. This paper proposes a novel approach for indoor robot localization that leverages a fusion of information from single-chip infrared (Time-of-Flight) and radar sensors. The aim of our research is the development of a cost-effective and lightweight system that can achieve high-precision robot localization. Unlike traditional localization methods based on LiDARs or cameras, our proposed system uses single-chip infrared and radar sensors to overcome the limitations of high cost and bulky hardware. Specifically, we employ a Doppler radar-based velocity motion model for the estimation of the robot's ego-motion, eliminating the need for additional sensors such as IMU or wheel encoders. Next, we describe a hybrid sensor model for single-chip infrared and radar sensors that provides robust and accurate environmental perception with dynamic outlier removal. Finally, we integrate these components into a Monte Carlo localization framework to generate accurate real-time estimation of the robot's position and orientation. This is the first time a single-chip infrared and radar fusion based framework has been applied to robot localization, to the best of our knowledge. Through a comprehensive experimental evaluation, we demonstrate the system's high accuracy and efficiency, achieving an average localization error of 9 cm in diverse indoor environments. This remarkable performance, combined with the low-cost and lightweight nature of our proposed solution, positions it as a highly promising alternative for a wide range of applications, including robotics, smart homes, and autonomous vehicles. The significant advancements of this novel approach offer vast potential to revolutionize the field of localization, enabling more precise and cost-effective navigation systems.
Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping
(2025)
Simultaneous localization and mapping is a critical capability for autonomous systems. Traditional SLAM approaches often rely on visual or LiDAR sensors and face significant challenges in adverse conditions such as low light or featureless environments. To overcome these limitations, we propose a novel Doppler-aided radar-inertial and LiDAR-inertial SLAM framework that leverages the complementary strengths of 4D radar, FMCW LiDAR, and inertial measurement units. Our system integrates Doppler velocity measurements and spatial data into a tightly-coupled front-end and graph optimization back-end to provide enhanced ego velocity estimation, accurate odometry, and robust mapping. We also introduce a Doppler-based scan-matching technique to improve front-end odometry in dynamic environments. In addition, our framework incorporates an innovative online extrinsic calibration mechanism, utilizing Doppler velocity and loop closure to dynamically maintain sensor alignment. Extensive evaluations on both public and proprietary datasets show that our system significantly outperforms state-of-the-art radar-SLAM and LiDAR-SLAM frameworks in terms of accuracy and robustness. To encourage further research, the code of our Doppler-SLAM and our dataset are available at: https://github.com/Wayne-DWA/Doppler-SLAM .
Heterogeneous catalysis, a process in which the reaction of gaseous or liquid chemical reagents is facilitated at the surface of a solid material, is responsible for the majority of industrial-scale chemical and fuel production reactions. The energy required to drive these reactions has historically been derived from the combustion of non-renewable fossil fuels and carries an unavoidably large carbon footprint. More recently, the development of environmentally responsible and sustainable chemical industries is increasingly motivated by greenhouse gas-induced climate change, thus creating demand for eco-friendly heterogeneous catalytic processes. This includes innovative approaches enabled by renewable forms of energy, such as the electrification of chemical and petrochemical processes, utilization of CO2 as a feedstock and the incorporation of light into catalytic reactions. Herein we review the conversion of solar energy to chemical energy using CO2, and describe how the photophysical and photochemical properties of nanostructured metal oxide photocatalysts have been engineered to efficiently incorporate light into heterogeneous gas–solid CO2 hydrogenation reactions. Realizing high photonic and energy efficiencies in these systems has demanded innovation in not only photocatalyst engineering, but also photoreactor and process engineering. Rather than exclusively providing an in-depth discussion of the chemistry and science within each individual study, this Tutorial Review highlights the multidisciplinary character of photocatalysis studies by covering the four essential components of a typical research work in this field (materials engineering, theoretical modelling, reactor engineering and process development) via case studies of the archetypal indium oxide catalyst materials. Through advances in these four components, progress has been made towards the ultimate goal of industrializing the production of CO2-derived chemicals and fuels.