Multi-Correlation Siamese Transformer Network with Dense Connection for 3D Single Object Tracking

Point cloud-based 3D object tracking is an important task in autonomous driving. Though great advances regarding Siamese-based 3D tracking have been made recently, it remains challenging to learn the correlation between the template and search branches effectively with the sparse LIDAR point cloud data. Instead of performing correlation of the two branches at just one point in the network, in this paper, we present a multi-correlation Siamese Transformer network that has multiple stages and carries out feature correlation at the end of each stage based on sparse pillars. More specifically, in each stage, self-attention is first applied to each branch separately to capture the non-local context information. Then, cross-attention is used to inject the template information into the search area. This strategy allows the feature learning of the search area to be aware of the template while keeping the individual characteristics of the template intact. To enable the network to easily preserve the information learned at different stages and ease the optimization, for the search area, we densely connect the initial input sparse pillars and the output of each stage to all subsequent stages and the target localization network, which converts pillars to bird's eye view (BEV) feature maps and predicts the state of the target with a small densely connected convolution network. Deep supervision is added to each stage to further boost the performance as well. The proposed algorithm is evaluated on the popular KITTI, nuScenes, and Waymo datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art. Ablation study that shows the effectiveness of each component is provided as well. Code is available at https://github.com/liangp/MCSTN-3DSOT.

Overconstrained Robotic Limb with Energy-Efficient, Omni-directional Locomotion

This paper studies the design, modeling, and control of a novel quadruped, featuring overconstrained robotic limbs employing the Bennett linkage for motion and power transmission. The modular limb design allows the robot to morph into reptile- or mammal-inspired forms. In contrast to the prevailing focus on planar limbs, this research delves into the classical overconstrained linkages, which have strong theoretical foundations in advanced kinematics but limited engineering applications. The study showcases the morphological superiority of overconstrained robotic limbs that can transform into planar or spherical limbs, exemplifying the Bennett linkage. By conducting kinematic and dynamic modeling, we apply model predictive control to simulate a range of locomotion tasks, revealing that overconstrained limbs outperform planar designs in omni-directional tasks like forward trotting, lateral trotting, and turning on the spot when considering foothold distances. These findings highlight the biological distinctions in limb design between reptiles and mammals and represent the first documented instance of overconstrained robotic limbs outperforming planar designs in dynamic locomotion.