Multi-modal fusion plays a critical role in 3D object detection, overcoming the inherent limitations of single-sensor perception in autonomous driving. Most fusion methods require data from high-resolution cameras and LiDAR sensors, which are less robust and the detection accuracy drops drastically with the increase of range as the point cloud density decreases. Alternatively, the fusion of Radar and LiDAR alleviates these issues but is still a developing field, especially for 4D Radar with a more robust and broader detection range. Nevertheless, different data characteristics and noise distributions between two sensors hinder performance improvement when directly integrating them. Therefore, we are the first to propose a novel fusion method termed $M^{2}$ -Fusion for 4D Radar and LiDAR, based on Multi-modal and Multi-scale fusion. To better integrate two sensors, we propose an Interaction-based Multi-Modal Fusion (IMMF) method utilizing a self-attention mechanism to learn features from each modality and exchange intermediate layer information. Specific to the current single-resolution voxel division's precision and efficiency balance problem, we also put forward a Center-based Multi-Scale Fusion (CMSF) method to first regress the center points of objects and then extract features in multiple resolutions. Furthermore, we present a data preprocessing method based on Gaussian distribution that effectively decreases data noise to reduce errors caused by point cloud divergence of 4D Radar data in the $x$ - $z$ plane. To evaluate the proposed fusion method, a series of experiments were conducted using the Astyx HiRes 2019 dataset, including the calibrated 4D Radar and 16-line LiDAR data. The results demonstrated that our fusion method compared favorably with state-of-the-art algorithms. When compared to PointPillars, our method achieves mAP (mean average precision) increases of 5.64 $\%$ and 13.57 $\%$ for 3D and BEV (bird's eye view) detection of the car class at a moderate level, respectively.

3D Multi-object tracking (MOT) empowers mobile robots to accomplish well-informed motion planning and navigation tasks by providing motion trajectories of surrounding objects. However, existing 3D MOT methods typically employ a single similarity metric and physical model to perform data association and state estimation for all objects. With large-scale modern datasets and real scenes, there are a variety of object categories that commonly exhibit distinctive geometric properties and motion patterns. In this way, such distinctions would enable various object categories to behave differently under the same standard, resulting in erroneous matches between trajectories and detections, and jeopardizing the reliability of downstream tasks (navigation, etc.). Towards this end, we propose Poly-MOT, an efficient 3D MOT method based on the Tracking-By-Detection framework that enables the tracker to choose the most appropriate tracking criteria for each object category. Specifically, Poly-MOT leverages different motion models for various object categories to characterize distinct types of motion accurately. We also introduce the constraint of the rigid structure of objects into a specific motion model to accurately describe the highly nonlinear motion of the object. Additionally, we introduce a two-stage data association strategy to ensure that objects can find the optimal similarity metric from three custom metrics for their categories and reduce missing matches. On the NuScenes dataset, our proposed method achieves state-of-the-art performance with 75.4% AMOTA. The code is available at https://github.com/lixiaoyu20001P0Iy-MOT.

3D object detection is becoming indispensable for environmental perception in autonomous driving. LiDAR point clouds often fail to distinguish objects with similar structures and are quite sparse for distant or small objects thereby introducing false and missed detections. To address these issues, LiDAR is often fused with cameras due to the rich textural information provided by images. However, current fusion methods suffer the inefficient data representation and inaccurate alignment of heterogeneous features, leading to poor precision and low efficiency. To this end, we propose a plug-and-play module termed range-image fusion (RI-Fusion) to achieve an effective fusion of LiDAR and camera data, designed to be easily accessible by existing mainstream LiDAR-based algorithms. In this process, we design an image and point cloud alignment method by converting a point cloud into a compact range view representation through a spherical coordinate transformation. The range image is then integrated with a corresponding camera image utilizing an attention mechanism. The original range image is then concatenated with fusion features to retain point cloud information, and the results are projected onto a spatial point cloud. Finally, the feature-enhanced point cloud can be input into a LiDAR-based 3D object detector. The results of validation experiments involving the KITTI 3D object detection benchmark showed that our proposed fusion method significantly enhanced multiple mainstream LiDAR-based 3D object detectors, PointPillars, SECOND, and Part A ² , improving the 3D mAP by 3.61%, 2.98%, and 1.27%, respectively, particularly for small objects such as pedestrians and cyclists.

3D Multi-Object Tracking (MOT) captures stable and comprehensive motion states of surrounding obstacles, essential for robotic perception. However, current 3D trackers face issues with accuracy and latency consistency. In this letter, we propose Fast-Poly, a fast and effective filter-based method for 3D MOT. Building upon our previous work Poly-MOT, Fast-Poly addresses object rotational anisotropy in 3D space, enhances local computation densification, and leverages parallelization technique, improving inference speed and precision. Fast-Poly is extensively tested on two large-scale tracking benchmarks with Python implementation. On the nuScenes dataset, Fast-Poly achieves new state-of-the-art performance with 75.8% AMOTA among all methods and can run at 34.2 FPS on a personal CPU. On the Waymo dataset, Fast-Poly exhibits competitive accuracy with 63.6% MOTA and impressive inference speed (35.5 FPS).

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, LiDAR inertial odometry (LIO) becomes increasingly prevalent in robotics and autonomous driving. However, many current SLAM systems lack sufficient adaptability to various scenarios. Challenges include decreased point cloud accuracy with longer frame intervals under the constant velocity assumption, coupling of erroneous IMU information when IMU saturation occurs, and decreased localization accuracy due to the use of fixed-resolution maps during indoor-outdoor scene transitions. To address these issues, we propose a loosely coupled adaptive LIO named Adaptive-LIO, which incorporates adaptive segmentation to enhance mapping accuracy, adapts motion modality through IMU saturation and fault detection, and adjusts map resolution adaptively using multiresolution voxel maps based on the distance from the LiDAR center. Our proposed method has been tested in various challenging scenarios, demonstrating the effectiveness of the improvements we introduce. The code is open-source on GitHub: Adaptive-LIO.