N$^{3}$-Mapping: Normal Guided Neural Non-Projective Signed Distance Fields for Large-scale 3D Mapping

Accurate and dense mapping in large-scale environments is essential for various robot applications. Recently, implicit neural signed distance fields (SDFs) have shown promising advances in this task. However, most existing approaches employ projective distances from range data as SDF supervision, introducing approximation errors and thus degrading the mapping quality. To address this problem, we introduce N3-Mapping, an implicit neural mapping system featuring normal-guided neural non-projective signed distance fields. Specifically, we directly sample points along the surface normal, instead of the ray, to obtain more accurate non-projective distance values from range data. Then these distance values are used as supervision to train the implicit map. For large-scale mapping, we apply a voxel-oriented sliding window mechanism to alleviate the forgetting issue with a bounded memory footprint. Besides, considering the uneven distribution of measured point clouds, a hierarchical sampling strategy is designed to improve training efficiency. Experiments demonstrate that our method effectively mitigates SDF approximation errors and achieves state-of-the-art mapping quality compared to existing approaches.

Progressive Dual Priori Network for Generalized Breast Tumor Segmentation

To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast amd irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different sites. The PDPNet first cropped tumor regions with a coarse-segmentation based localization module, then the breast tumor mask was progressively refined by using the weak semantic priori and cross-scale correlation prior knowledge. To validate the effectiveness of PDPNet, we compared it with several state-of-the-art methods on multi-center datasets. The results showed that, comparing against the suboptimal method, the DSC, SEN, KAPPA and HD95 of PDPNet were improved 3.63\%, 8.19\%, 5.52\%, and 3.66\% respectively. In addition, through ablations, we demonstrated that the proposed localization module can decrease the influence of normal tissues and therefore improve the generalization ability of the model. The weak semantic priors allow focusing on tumor regions to avoid missing small tumors and low-contrast tumors. The cross-scale correlation priors are beneficial for promoting the shape-aware ability for irregual tumors. Thus integrating them in a unified framework improved the multi-center breast tumor segmentation performance.

How to Fine-tune the Model: Unified Model Shift and Model Bias Policy Optimization

Designing and deriving effective model-based reinforcement learning (MBRL) algorithms with a performance improvement guarantee is challenging, mainly attributed to the high coupling between model learning and policy optimization. Many prior methods that rely on return discrepancy to guide model learning ignore the impacts of model shift, which can lead to performance deterioration due to excessive model updates. Other methods use performance difference bound to explicitly consider model shift. However, these methods rely on a fixed threshold to constrain model shift, resulting in a heavy dependence on the threshold and a lack of adaptability during the training process. In this paper, we theoretically derive an optimization objective that can unify model shift and model bias and then formulate a fine-tuning process. This process adaptively adjusts the model updates to get a performance improvement guarantee while avoiding model overfitting. Based on these, we develop a straightforward algorithm USB-PO (Unified model Shift and model Bias Policy Optimization). Empirical results show that USB-PO achieves state-of-the-art performance on several challenging benchmark tasks.

VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition

LiDAR-based place recognition plays a crucial role in Simultaneous Localization and Mapping (SLAM) and LiDAR localization. Despite the emergence of various deep learning-based and hand-crafting-based methods, rotation-induced place recognition failure remains a critical challenge. Existing studies address this limitation through specific training strategies or network structures. However, the former does not produce satisfactory results, while the latter focuses mainly on the reduced problem of SO(2) rotation invariance. Methods targeting SO(3) rotation invariance suffer from limitations in discrimination capability. In this paper, we propose a new method that employs Vector Neurons Network (VNN) to achieve SO(3) rotation invariance. We first extract rotation-equivariant features from neighboring points and map low-dimensional features to a high-dimensional space through VNN. Afterwards, we calculate the Euclidean and Cosine distance in the rotation-equivariant feature space as rotation-invariant feature descriptors. Finally, we aggregate the features using GeM pooling to obtain global descriptors. To address the significant information loss when formulating rotation-invariant descriptors, we propose computing distances between features at different layers within the Euclidean space neighborhood. This greatly improves the discriminability of the point cloud descriptors while ensuring computational efficiency. Experimental results on public datasets show that our approach significantly outperforms other baseline methods implementing rotation invariance, while achieving comparable results with current state-of-the-art place recognition methods that do not consider rotation issues.

LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

Local geometric information, i.e. normal and point distribution, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constrains for data association, which further determines the direction of optimization and ultimately affects the accuracy of poses. However, estimating normal and point distribution are time-consuming tasks even with the assistance of the KDtree or volumetic maps. To achieve fast normal estimation, we look into the structural information of LiDAR scan and propose a novel fast approximate least squares (FALS) method. With the pre-computed bearing information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update its point cloud distribution incrementally while maintaining its consistency with the normals. For scan points that satisfy visibility and consistency checks based on normal, we devise a robust and accurate hierarchical data association schema considering the distribution where point-to-surfel is prioritized over point-to-plane. We further fix voxels after the distribution convergences to balance the time consumption and the correctness of representation. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods.

Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery

Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend to adopt overly conservative policy to avoid unsafe situations. However, overly conservative policy severely hinders the exploration, and makes the algorithms substantially less rewarding. In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states. The boundary we construct is equivalent to distinguishing dead-end states, indicating the maximum extent to which safe exploration is guaranteed, and thus has minimum limitation on exploration. Similar to Recovery Reinforcement Learning, we utilize a decoupled RL framework to learn two policies, (1) a task policy that only considers improving the task performance, and (2) a recovery policy that maximizes safety. The recovery policy and a corresponding safety critic are pretrained on an offline dataset, in which the safety critic evaluates upper bound of safety in each state as awareness of environmental safety for the agent. During online training, a behavior correction mechanism is adopted, ensuring the agent to interact with the environment using safe actions only. Finally, experiments of continuous control tasks demonstrate that our approach has better task performance with less safety violations than state-of-the-art algorithms.

Learning Sequence Descriptor based on Spatiotemporal Attention for Visual Place Recognition

Sequence-based visual place recognition (sVPR) aims to match frame sequences with frames stored in a reference map for localization. Existing methods include sequence matching and sequence descriptor-based retrieval. The former is based on the assumption of constant velocity, which is difficult to hold in real scenarios and does not get rid of the intrinsic single frame descriptor mismatch. The latter solves this problem by extracting a descriptor for the whole sequence, but current sequence descriptors are only constructed by feature aggregation of multi-frames, with no temporal information interaction. In this paper, we propose a sequential descriptor extraction method to fuse spatiotemporal information effectively and generate discriminative descriptors. Specifically, similar features on the same frame focu on each other and learn space structure, and the same local regions of different frames learn local feature changes over time. And we use sliding windows to control the temporal self-attention range and adpot relative position encoding to construct the positional relationships between different features, which allows our descriptor to capture the inherent dynamics in the frame sequence and local feature motion.

LIMOT: A Tightly-Coupled System for LiDAR-Inertial Odometry and Multi-Object Tracking

Simultaneous localization and mapping (SLAM) is critical to the implementation of autonomous driving. Most LiDAR-inertial SLAM algorithms assume a static environment, leading to unreliable localization in dynamic environments. Furthermore, accurate tracking of moving objects is of great significance for the control and planning of autonomous vehicle operation. This study proposes LIMOT, a tightly-coupled multi-object tracking and LiDAR-inertial SLAM system capable of accurately estimating the poses of both ego-vehicle and objects. First, we use 3D bounding boxes generated by an object detector to represent all movable objects and perform LiDAR odometry using inertial measurement unit (IMU) pre-integration result. Based on the historical trajectories of tracked objects in a sliding window, we perform robust object association. We propose a trajectory-based dynamic feature filtering method, which filters out features belonging to moving objects by leveraging tracking results. Factor graph-based optimization is then conducted to optimize the bias of the IMU and the poses of both the ego-vehicle and surrounding objects in a sliding window. Experiments conducted on KITTI datasets show that our method achieves better pose and tracking accuracy than our previous work DL-SLOT and other SLAM and multi-object tracking baseline methods.

DL-SLOT: Dynamic LiDAR SLAM and object tracking based on collaborative graph optimization

Ego-pose estimation and dynamic object tracking are two critical problems for autonomous driving systems. The solutions to these problems are generally based on their respective assumptions, \ie{the static world assumption for simultaneous localization and mapping (SLAM) and the accurate ego-pose assumption for object tracking}. However, these assumptions are challenging to hold in dynamic road scenarios, where SLAM and object tracking become closely correlated. Therefore, we propose DL-SLOT, a dynamic LiDAR SLAM and object tracking method, to simultaneously address these two coupled problems. This method integrates the state estimations of both the autonomous vehicle and the stationary and dynamic objects in the environment into a unified optimization framework. First, we used object detection to identify all points belonging to potentially dynamic objects. Subsequently, a LiDAR odometry was conducted using the filtered point cloud. Simultaneously, we proposed a sliding window-based object association method that accurately associates objects according to the historical trajectories of tracked objects. The ego-states and those of the stationary and dynamic objects are integrated into the sliding window-based collaborative graph optimization. The stationary objects are subsequently restored from the potentially dynamic object set. Finally, a global pose-graph is implemented to eliminate the accumulated error. Experiments on KITTI datasets demonstrate that our method achieves better accuracy than SLAM and object tracking baseline methods. This confirms that solving SLAM and object tracking simultaneously is mutually advantageous, dramatically improving the robustness and accuracy of SLAM and object tracking in dynamic road scenarios.

DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.