Recently, 4D millimetre-wave radar exhibits more stable perception ability than LiDAR and camera under adverse conditions (e.g. rain and fog). However, low-quality radar points hinder its application, especially the odometry task that requires a dense and accurate matching. To fully explore the potential of 4D radar, we introduce a learning-based odometry framework, enabling robust ego-motion estimation from finite and uncertain geometry information. First, for sparse radar points, we propose a local completion to supplement missing structures and provide denser guideline for aligning two frames. Then, a context-aware association with a hierarchical structure flexibly matches points of different scales aided by feature similarity, and improves local matching consistency through correlation balancing. Finally, we present a window-based optimizer that uses historical priors to establish a coupling state estimation and correct errors of inter-frame matching. The superiority of our algorithm is confirmed on View-of-Delft dataset, achieving around a 50% performance improvement over previous approaches and delivering accuracy on par with LiDAR odometry. The code will be released at https://github.com/NEU-REAL/CAO-RONet.

Accurate point cloud information is important for robot perception and autonomous driving. Although advanced 4D radar can provide point cloud with higher resolution than 3D radar, its data still contains a significant amount of noise due to measurement principle. To solve this issue, we propose RDN (Radar Denoising Network), a denoising network specifically designed for 4D radar. RDN includes three innovative modules: First, to overcome the noisy nature of radar points, we design a feature similarity-based farthest point sampling module (FS-FPS), which can extract representative sampling points from the noisy point cloud. Secondly, to address feature propagation issues caused by the sparse and long-range characteristics of 4D radar points, we introduce a virtual feature point prediction (VFP) module and an iterative upsampling (IUS) module. The VFP module generates virtual feature points through the network to serve as bridges for information transmission, while the IUS module uses an iterative approach to gradually refine feature propagation. The experiments on MSC-RAD4D and NTU4DRadLM datasets demonstrate the effectiveness and generalization of our method. Besides, odometry experiments prove the practical value of point cloud denoising in improving robot perception.

Vehicle localization is a critical component in the planning and navigation of autonomous driving system. Generally, traditional vehicle localization methods rely on the Global Navigation Satellite System (GNSS) for self-localization. Unfortunately, GNSS can become unreliable and may fail in urban canyons, under trees, and beneath overpasses. To address this problem, we propose a visual localization framework assisted by offline Google satellite maps in GNSS-weak or GNSS-denied environments. And we introduce learning-based ground-to-satellite map feature matching method to mitigate the long-term cumulative drift of visual odometry. To reduce the negative impact of cross-view matching errors on localization accuracy, we propose a novel cross-view pose selection method to build two pose uncertainty models. Moreover, we combine the proposed method with classical SLAM methods to develop a vehicle localization framework. To verify the performance of the proposed method, we carried out the accuracy comparison experiment with state-of-the-art fusion localization methods and feature matching methods. Experimental results indicate that the proposed method achieves the best localization performance compared with the state-of-the-art methods, and our method achieves the root mean square error of 0.290m and 0.014rad in KITTI-05. The implementation code of this paper will be open-source at https://github.com/NEU-REAL/visualLocalization-with-satelliteMap.

In the process of urban environment mapping, the sequential accumulations of dynamic objects will leave a large number of traces in the map. These traces will usually have bad influences on the localization accuracy and navigation performance of the robot. Therefore, dynamic objects removal plays an important role for creating clean map. However, conventional dynamic objects removal methods usually run offline. That is, the map is reprocessed after it is constructed, which undoubtedly increases additional time costs. To tackle the problem, this paper proposes a novel method for online dynamic objects removal for ground vehicles. According to the observation time difference between the object and the ground where it is located, dynamic objects are classified into two types: suddenly appear and suddenly disappear. For these two kinds of dynamic objects, we propose downward retrieval and upward retrieval methods to eliminate them respectively. We validate our method on SemanticKITTI dataset and author-collected dataset with highly dynamic objects. Compared with other state-of-the-art methods, our method is more efficient and robust, and reduces the running time per frame by more than 60% on average. Our method will be open-sourced on GitHub1.

In complex indoor structural scenes such as shopping centers and malls, camera pose estimation using pure point features is easy to fail due to the difficulty in extracting sufficient and stable point features from weak textures or dynamic environments. Recent works have attempted to address these challenges by introducing line features. However, the addition of line features increases the number of parameters and landmarks for BA (Bundle Adjustment), leading to efficiency reduction. This is a common issue in multi-modal SLAM (Simultaneous Localization And Mapping). To address this issue, this paper proposes a novel visual-depth-inertial odometry (ASML-VDIO) framework by combining RGB-D and IMU sensors. To improve the efficiency of BA, the proposed landmark classification method classifies 3D landmarks into accurate landmarks and other landmarks based on spatial consistency verification and depth range limitation. Then, accurate landmarks are fixed, and only other landmarks are optimized in the optimization of BA. Furthermore, to remove line features extracted from dynamic objects (pedestrian, shopping-car, etc), we propose a dynamic line removal method that combines geometric constraints and motion constraints of line features. Finally, the method is evaluated on public and author-collected datasets, showing competitive accuracy and robustness in complex indoor structural scenes while 71% speedup on optimization thread with same constraints.

In recent years, the interest in underwater exploration with Autonomous Underwater Vehicles (AUVs) equipped with side-scan sonars (SSS) has grown considerably. However, state-of-the-art SSS Simultaneous Localization and Mapping (SLAM) systems encounter challenges in data association across large viewpoint changes. Additionally, these systems assume that the seabed is a flat surface, leading to significant mapping error in uneven underwater terrains. To address these challenges, we propose a framework that leverages the side-scan sonar geometry to facilitate data association and improve mapping accuracy. The framework begins with a preprocessing module that extracts feature points and provides initial estimates of the elevation angles of the landmarks. Then, a non-consecutive data association module applies epipolar line search to establish correspondences between the current and historical frames. Finally, the mapping module uses side-scan sonar bundle adjustment to recover the positions of the landmarks. The proposed method is evaluated using an underwater terraced fields dataset. Our method achieves over 90% matching rate and reduces the average mapping error from 3.799 to 0.134.

Three-dimensional point cloud map plays an important role in 3-D reconstruction, autonomous robot navigation, autonomous driving, and environmental monitoring. Nowadays, 3-D point cloud map could be obtained through frame-by-frame accumulation of LiDAR point cloud using SLAM technology. However, during this process, the movements of dynamic objects in the environment will leave a large number of traces on the point cloud map, causing difficulties in the subsequent use of the map, such as city model construction and robot autonomous navigation. Therefore, dynamic traces removal is crucial for building clean static maps. However, existing methods for dynamic traces removal are mostly offline, which inevitably incurs additional time consumption. To address this problem, this article proposes an online dynamic traces removal method. We take voxels as the smallest unit for dynamic traces removal, and voxels containing dynamic traces are called dynamic voxels, otherwise they are called static voxels. Our method is based on the assumption that static voxels always appear and disappear simultaneously with the ground below them. Therefore, we call voxel that appears later than the ground as suddenly appear dynamic voxel, and voxel that disappears earlier than the ground as suddenly disappear dynamic voxel. We call this method of judging dynamic voxels as observation time difference, and propose downward retrieval and upward retrieval methods to remove these two types of dynamic voxels, respectively. We tested our proposed method on SemanticKITTI, UrbanLoco, and author-collected datasets. Experimental results show that our method is more accurate and robust than existing online dynamic traces removal methods. And compared with other methods, our method shortens the time of processing each frame of point cloud by more than 60%. Our method is open-sourced on GitHub:https://github.com/RongguangWu/OTD.