Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub**https://github.com/SenseRoboticsLab/DBE-Radar.

Underwater environments pose significant challenges for visual simultaneous localization and mapping (SLAM) systems due to limited visibility, inadequate illumination, and sporadic loss of structural features in images. Addressing these challenges, this article introduces a novel, tightly coupled acoustic-visual-inertial SLAM approach, termed AQUA-SLAM, to fuse a Doppler velocity log (DVL), a stereo camera, and an inertial measurement unit (IMU) within a graph optimization framework. Moreover, we propose an efficient sensor calibration technique, encompassing the multisensor extrinsic calibration (among the DVL, camera, and IMU) and the DVL transducer misalignment calibration, with a fast linear approximation procedure for real-time online execution. The proposed methods are extensively evaluated in a tank environment with ground truth, and validated for offshore applications in the North Sea. The results demonstrate that our method surpasses current state-of-the-art underwater and visual-inertial SLAM systems in terms of localization accuracy and robustness. The proposed system will be made open-source for the community.

This paper studies 3D LiDAR mapping with a focus on developing an updatable and localizable map representation that enables continuity, compactness and consistency in 3D maps. Traditional LiDAR Simultaneous Localization and Mapping (SLAM) systems often rely on 3D point cloud maps, which typically require extensive storage to preserve structural details in large-scale environments. In this paper, we propose a novel paradigm for LiDAR SLAM by leveraging the Continuous and Ultra-compact Representation of LiDAR (CURL) introduced in [1]. Our proposed LiDAR mapping approach, CURL-SLAM, produces compact 3D maps capable of continuous reconstruction at variable densities using CURL's spherical harmonics implicit encoding, and achieves global map consistency after loop closure. Unlike popular Iterative Closest Point (ICP)-based LiDAR odometry techniques, CURL-SLAM formulates LiDAR pose estimation as a unique optimization problem tailored for CURL and extends it to local Bundle Adjustment (BA), enabling simultaneous pose refinement and map correction. Experimental results demonstrate that CURL-SLAM achieves state-of-the-art 3D mapping quality and competitive LiDAR trajectory accuracy, delivering sensor-rate real-time performance (10 Hz) on a CPU. We will release the CURL-SLAM implementation to the community.

This paper introduces a novel sonar odometry system that estimates the relative spatial transformation between two sonar image frames. Considering the unique challenges, such as low resolution and high noise, of sonar imagery for odometry and Simultaneous Localization and Mapping (SLAM), the proposed Direct Imaging Sonar Odometry (DISO) system is designed to estimate the relative transformation between two sonar frames by minimizing the aggregated sonar intensity errors of points with high intensity gradients. Moreover, DISO is implemented to incorporate a multi-sensor window optimization technique, a data association strategy and an acoustic intensity outlier rejection algorithm for reliability and accuracy. The effectiveness of DISO is evaluated using both simulated and real-world sonar datasets, showing that it outperforms the existing geometric-only method on localization accuracy and achieves state-of-the-art sonar odometry performance. We release the source codes of the DISO implementation to the community. The source code is available at https://github.com/SenseRoboticsLab/DISO.

Maps of LiDAR Simultaneous Localisation and Mapping (SLAM) are often represented as point clouds. They usually take up a huge amount of storage space for large-scale environments, otherwise much structural detail may not be kept. In this paper, a novel paradigm of LiDAR mapping and odometry is designed by leveraging the Continuous and Ultra-compact Representation of LiDAR (CURL) proposed in [1]. Termed CURL-MAP (Mapping and Positioning), the proposed approach can not only reconstruct 3D maps with a continuously varying density but also efficiently reduce map storage space by using CURL’s spherical harmonics implicit encoding. Different from the popular Iterative Closest Point (ICP) based LiDAR odometry techniques, CURL-MAP formulates LiDAR pose estimation as a unique optimisation problem tailored for CURL. Experiment evaluation shows that CURL-MAP achieves state-of-the-art 3D mapping results and competitive LiDAR odometry accuracy. We will release the CURL-MAP codes for the community.

In this paper, we propose using online public maps, e.g., OpenStreetMap (OSM), for large-scale radar-based localization without needing a prior sensing map. This can potentially extend the localization system to anywhere worldwide without building, saving, or maintaining a sensing map, as long as an online public map covers the operating area. Existing methods using OSM only use route network or semantics information. These two sources of information are not combined in the previous works, while our proposed system fuses them to improve localization accuracy. Our experiments, on three open datasets collected from three different continents, show that the proposed system outperforms the state-of-the-art localization methods, reducing up to 50% of position errors. We release an open-source implementation for the community.

This paper presents an adaptive heading approach for perception awareness during trajectory following. By adapting the heading of a robot to improve the feature tracking in the current mapped environment, the accuracy in localisation can be improved. This can have a significant advantage for autonomous operations in GPS-denied environments such as subsea or in caves. The aim of the proposed approach is to position the sensor used for perception and feature tracking in such a way that it; obtains a view that contains a good observation of the previously mapped environment, face forward along the direction of travel, reduces the change in heading and view the perceived environment along the surface's estimated normals. These 4 objectives create a weighted utility function that is used to find the most beneficial heading. The benefit is a system that improves feature tracking for simultaneous localisation and mapping (SLAM) while considering the safety of the robot by being aware of its surrounding. To sense the environment, a simulated sensor is discretised to a set of vertical rays based on the vertical field of view. The vertical rays are swept 360 degrees around a position to evaluate for a new heading. This allows for the simulated sensor data from ray casting to be reused and therefore reduces the computational load to find the heading which maximises the utility function. The paper is focused on holonomic robots capable of controlling the robot's heading or sensor orientation independently from the position. We present results and evaluation in a simulated environment where we show a great improvement in the SLAM's pose estimation. In addition, we endow an autonomous underwater vehicle (AUV) with the proposed approach during field trials and present the result in two different environments.

The extrinsic parameters play a crucial role in multi-sensor fusion, such as visual-inertial Simultaneous Localization and Mapping(SLAM), as they enable the accurate alignment and integration of measurements from different sensors. However, extrinsic calibration is challenging in scenarios, such as underwater, where in-view structures are scanty and visibility is limited, causing incorrect extrinsic calibration due to insufficient motion on all degrees of freedom. In this paper, we propose an entropy-based active extrinsic calibration algorithm leverages observability analysis and information entropy to enhance the accuracy and reliability of extrinsic calibration. It determines the system observability numerically by using singular value decomposition (SVD) of the Fisher Information Matrix (FIM). Furthermore, when the extrinsic parameter is not fully observable, our method actively searches for the next best motion to recover the system's observability via entropy-based optimization. Experimental results on synthetic data, in a simulation, and using an actual underwater vehicle verify that the proposed method is able to avoid the calibration failure while improving the calibration accuracy and reliability.

One of the main problems in the agricultural picking field is to realize all-terrain map construction and autonomous navigation. Many picking scenes are characterized by many potholes, many slopes, and complex actual environment. In the actual automatic picking process, picking robots have certain ability to pass over obstacles, and can pass through some of the potholes, while traditional two-dimensional maps cannot effectively identify and judge obstacles, which is difficult to achieve good optimal path planning. Based on this problem, we propose a ground 3D map extraction and reconstruction method based on 3D point cloud data. In the target area, firstly, the UAV is equipped with laser radar for 3D point cloud data acquisition, the collected point cloud data are de-noised, do ground segmentation and extraction and point cloud simplification, and the 3D point cloud map is reconstructed to provide autonomous navigation 3D map for mobile picking robots.

Underwater scenarios are challenging for visual Simultaneous Localization and Mapping (SLAM) due to limited visibility and intermittently losing structures in image views. In this paper, we propose a visual acoustic bundle adjustment system which fuses a camera and a Doppler Velocity Log (DVL) in a graph SLAM framework for reliable underwater localization and mapping. In order to fuse the vision with the acoustic measurements, an calibration algorithm is also designed to estimate extrinsic parameters between a camera and a DVL using features detected in scenes. Experimental results in a tank and an offshore wind farm show the proposed method can achieve better robustness and localization accuracy than pure visual SLAM, especially in visually challenging scenarios, and the extrinsic calibration parameters can be accurately estimated, even when initialized with a random guess.