LiDAR-inertial odometry is widely used in robotics navigation, autonomous driving, and drone operation to provide precise, low-latency motion estimation. Filter-based methods are fast but suffer from significant cumulative errors. Graph optimization methods reduce cumulative errors through loop closure detection but are computationally expensive. In this work, we propose LIO-DPC, a framework that combines the benefits of the filter-based approach and graph-based approach. First, we propose a dynamic pose chain optimization method. It generates an initial pose chain using the fast filter. This is followed by applying computationally efficient local graph optimization to a set of local pose chains to generate refined relative poses, which are then used to update the motion estimation. Second, we propose a loop sparsification approach to select representative loops that are both temporally and spatially proximate, to reduce the computational complexity in graph optimization and minimize loop errors. Extensive experiments demonstrate that LIO-DPC achieves real-time performance and outperforms state-of-the-art methods in accuracy.

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods.

Deep neural networks for 3D point clouds are receiving increasing attention. Recent works have shown that deep neural networks for 3D point clouds are vulnerable to adversarial attacks. However, existing adversarial attacks typically iteratively optimize a single sample to generate the adversarial point cloud, which requires exhausting computations. To make things worse, each point from the point cloud is processed indiscriminately, ignoring distinctions among points. To overcome the shortcomings mentioned above, we propose a method called SC-Net, which can generate an adversarial point cloud in a single forward pass. Specifically, SC-Net treats each point discriminatively by selecting salient points as attack targets. Furthermore, an elaborate Curvature-based distance loss is designed to constrain the strength of attacks to ensure surface consistency. Comparison and ablation experiments demonstrate SC-Net’s superior performance.

3D object tracking is the key of automatic driving. We propose a new 3D object tracking method called Transformer-Hausdorff Net (TH-Net). It contains three main modules: Feature Extraction, Feature Fusion, and Proposal Generation. The Feature Extraction module extracts features from the template and search area, where the permutation-invariable Transformers is leveraged to deal with the point cloud’s dis-order and sparsity. The features of template and search area are then fused by the Feature Fusion module to generate the tracking clues. Based on the tracking clues, we further generate 3D target proposal and execute verification in Proposal Generation module. TH-Net achieves a performance improvement in contrast to the state-of-the-art work on KITTI and NuScenes dataset.