Accurate ambient perception via multi-object detection and tracking is instrumental for autonomous vehicles. This article addresses two main challenges when operating solely on 3-D light laser detection and ranging (LiDAR) point clouds: the classification of objects with similar geometric structures and tracking under the commonplace setting of low-frequency sensing. First, we design a semantically enhanced feature aggregation module that fuses features learned from two branches with different resolutions and depths. Subsequently, the extracted semantic information combined with our proposed Margin Loss allows the re-identification module to extract time-invariant geometric features. These features are fused with the positional information provided by the detector by a cluster-based Earth's mover distance algorithm along with conflation to improve the tracking stability. Extensive experiments on nuScenes demonstrate that our proposed model outperforms the state-of-the-art methods for both LiDAR-based 3-D object detection and tracking. In particular, we report an increase of 1.1% in average multi-object tracking accuracy, as well higher mean average precision for detection by 6.2% and 7.5% on motorcycle and bicycle, respectively.

3D Multi-Object Tracking (MOT) plays a crucial role in efficient and safe operation of automatic driving, especially in scenarios of occlusion or poor visibility. Most 3D MOT methods leverage only positional distance, which is insufficient for scenes with high density of objects or drastic changes in the motion state. In order to address this, we propose a new 3D MOT model which fuses information pertaining to positional distance and geometric similarity. Our proposed solution comprises of four parts: a) a feature extraction mechanism integrated into a commonly used detector to extract individual features for each detection, b) computation of two distance matrices based on Euclidean distance and feature similarity, c) conversion of the distance matrices to probability matrices by a cluster based Earth-Mover Distance (EMD) algorithm, and d) a data association method that fuses both sources to boost the tracking accuracy. Our proposed model demonstrates state-of-the-art performance on the nuScenes tracking dataset, with extensive experiments attesting to an improved tracking accuracy over baselines that operate solely on positional distance.