@article{WangHaagHerraezetal.2025, author = {Wang, Dong and Haag, Hannes and Herraez, Daniel Casado and May, Stefan and Stachniss, Cyrill and N{\"u}chter, Andreas}, title = {Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping}, series = {IEEE Robotics and Automation Letters}, volume = {10}, journal = {IEEE Robotics and Automation Letters}, number = {9}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, issn = {2377-3766}, doi = {10.1109/LRA.2025.3595071}, pages = {9438 -- 9445}, year = {2025}, abstract = {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 .}, language = {en} } @inproceedings{WangMayNuechter2024, author = {Wang, Dong and May, Stefan and N{\"u}chter, Andreas}, title = {RIV-SLAM: Radar-Inertial-Velocity optimization based graph SLAM}, doi = {10.1109/CASE59546.2024.10711511}, pages = {774 -- 781}, year = {2024}, abstract = {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}, language = {de} } @inproceedings{VolletMayNuechter2023, author = {Vollet, Johannes and May, Stefan and N{\"u}chter, Andreas}, title = {Simultaneous 3D Reconstruction and Vegetation Classification Utilizing a Multispectral Stereo Camera}, series = {2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)}, booktitle = {2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)}, publisher = {IEEE}, doi = {10.1109/SSRR59696.2023.10499928}, pages = {132 -- 138}, year = {2023}, abstract = {Obstacle detection is crucial for ensuring the safety of autonomous robots and their surroundings in unstructured outdoor environments. Objects with minimal lateral dimensions can pose risks to the robot or serve as important elements in the infrastructure it operates in. Detecting these structures becomes particularly challenging when tall vegetation is present. Distinguishing between soft, traversable objects, such as tufts of grass, and potentially lethal solid obstacles is paramount to a robot's ability to operate. This paper presents a novel approach that focuses on point cloud generation and vegetation identification to facilitate the safe navigation of autonomous outdoor robots. Our approach uses a single multispectral stereo camera system that employs a novel stereo matching strategy based on binary descriptors for spectrally non-identical image pairs.}, language = {en} } @inproceedings{WangMasannekMayetal.2023, author = {Wang, Dong and Masannek, Marco and May, Stefan and N{\"u}chter, Andreas}, title = {Infradar-Localization: single-chip infrared- and radar-based Monte Carlo localization}, doi = {10.34646/thn/ohmdok-1050}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:92-opus4-10502}, pages = {8}, year = {2023}, abstract = {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.}, language = {en} } @inproceedings{KochEngelhardtMayetal.2020, author = {Koch, Philipp and Engelhardt, Helmut and May, Stefan and Schmidpeter, Michael and Ziegler, Jasmin and N{\"u}chter, Andreas}, title = {Signed Distance Based Reconstruction for Exploration and Change Detection in Underground Mining Disaster Prevention}, series = {2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)}, booktitle = {2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)}, publisher = {IEEE}, doi = {10.1109/SSRR50563.2020.9292618}, pages = {360 -- 365}, year = {2020}, abstract = {This publication describes an application of a Truncated Signed Distance Mapping approach for disaster intervention in underground mine shafts through geometrical change detection of the shaft walls. The paper describes two main problems of such an approach (aligning two potentially huge point clouds and automatic change detection by comparing the reconstructed volumes) and explains in detail the proposed solution.}, language = {en} }