The conversion of raw point clouds into pillar representations has been widely adopted for 3D object detection. Such conversion allows a point cloud to be discretized into structured grids, which enables more efficient spatial representation and faster processing in real-time autonomous driving systems. However, discretizing raw point clouds often leads to the misdetection of small objects such as pedestrians and cyclists. This is because the discretization inevitably results in the loss of contextual and multi-resolution information within raw point clouds. To address this issue, we propose PAPNet, a point-enhanced attention-aware pillar network mainly composed of a point-pillar cross-attention module (PCM), a pillar-wise dual attention module (PDAM), and a multi-resolution set abstraction module (MSAM). PCM integrates raw point cloud features with pillar features across different dimensions, and PDAM guides PAPNet to focus on the intrinsic characteristics of the pillars. Additionally, MSAM retains both high-resolution and low-resolution features while integrating multi-scale information. Extensive experiments on four public datasets and in real-world scenarios demonstrate the effectiveness and efficiency of PAPNet. Codes, data, and demo videos can be found at the project website https://vsislab.github.io/PAPNet/.

Autonomous navigation of unmanned ground vehicles (UGVs) in dynamic scenes is a challenging task that requires them to avoid obstacles and move toward the goal simultaneously. This paper proposes ALVO, an adaptive learning policy that leverages velocity obstacles for UGV navigation. ALVO employs an adaptive gating-based mechanism for reactive obstacle avoidance, which enables the UGV to either slow down or proactively navigate around obstacles based on the relative importance of the environmental state and the goal. A reward function based on velocity obstacles is also designed to guide the UGV to navigate toward the goal while avoiding obstacles. Extensive experiments demonstrate that ALVO outperforms the competing approaches in various dynamic environments. We also implemented our method on a real UGV and showed that it performed well in real-world scenarios.

Circular accessible depth (CAD) provides a lightweight and robust traversability representation for autonomous navigation of unmanned ground vehicles (UGV). Aiming at the limitations of existing LiDAR-based methods in detecting low-thickness targets and executing semantic reasoning, we propose VCADNet, a vision-based neural network for circular accessible depth prediction. VCADNet comprises three core components: a geometry-based query module for multi-view bird’s eye view feature extraction, a polar coordinate transformation for CAD alignment, and a multi-scale U-Net architecture for depth prediction. In addition, we present a cross-modal contrastive learning scheme to enhance the spatial reasoning of VCADNet, which transfers knowledge from LiDAR-based encoders to vision-based counterparts. Extensive experiments demonstrate the superior performance of VCADNet in various UGV perception tasks.

In this article, we present the circular accessible depth (CAD), a robust traversability representation for an unmanned ground vehicle (UGV) to learn traversability in various scenarios containing irregular obstacles. To predict CAD, we propose a neural network, namely CADNet, with an attention-based multiframe point cloud fusion module, stability-attention module (SAM), to encode the spatial features from point clouds captured by LiDAR. CAD is designed based on the polar coordinate system and focuses on predicting the border of traversable area. Since it encodes the spatial information of the surrounding environment, which enables a semisupervised learning for the CADNet, and thus, desirably avoids annotating a large amount of data. Extensive experiments demonstrate that CAD outperforms baselines in terms of robustness and precision. We also implement our method on a real UGV and show that it performs well in real-world scenarios.