TimePillars: Temporally-recurrent 3D LiDAR Object Detection

Abstract

Abstract Object detection applied to LiDAR point clouds is a relevant task in robotics, and particularly in autonomous driving. Single frame methods, predominant in the field, exploit information from individual sensor scans. Recent approaches achieve good performance, at relatively low inference time. Nevertheless, given the inherent high sparsity of LiDAR data, these methods struggle in long-range detection (e.g. 200m) and lack of temporal continuity, ignoring past information. We deem these characteristics to be critical in achieving safe automation. Aggregating past data frames not only leads to a denser point cloud representation, but it also brings time-awareness to the system, and provides information about how the environment is changing. Solutions of this kind, however, are often highly problem-specific, demand careful data processing, and tend not to fulfil efficiency requirements. In this context we propose TimePillars, a temporally-recurrent object detection pipeline which leverages the pillar representation of LiDAR data across time, respecting hardware integration efficiency constraints, and exploiting the diversity and long-range information of the novel Zenseact Open Dataset (ZOD). By performing extensive experimentation, we prove the benefits of having a recurrent scheme, and show how basic building blocks are enough to achieve robust and efficient results.