TimePillars: Temporally-Recurrent 3D LiDAR Object Detection
Ernesto Lozano Calvo Bernardo Taveira Fredrik Kahl Niklas Gustafsson Jonathan Larsson Adam Tonderski
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) which we deem to be critical in
achieving safe automation. Aggregating multiple scans 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 runtime 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).
Through experimentation, we prove the benefits of having recurrency, and show
how basic building blocks are enough to achieve robust and efficient results.