In this work, we propose a disruptively frugal LiDAR perception dataflow that
generates rather than senses parts of the environment that are either
predictable based on the extensive training of the environment or have limited
consequence to the overall prediction accuracy. Therefore, the proposed
methodology trades off sensing energy with training data for low-power robotics
and autonomous navigation to operate frugally with sensors, extending their
lifetime on a single battery charge. Our proposed generative pre-training
strategy for this purpose, called as radially masked autoencoding (R-MAE), can
also be readily implemented in a typical LiDAR system by selectively activating
and controlling the laser power for randomly generated angular regions during
on-field operations. Our extensive evaluations show that pre-training with
R-MAE enables focusing on the radial segments of the data, thereby capturing
spatial relationships and distances between objects more effectively than
conventional procedures. Therefore, the proposed methodology not only reduces
sensing energy but also improves prediction accuracy. For example, our
extensive evaluations on Waymo, nuScenes, and KITTI datasets show that the
approach achieves over a 5% average precision improvement in detection tasks
across datasets and over a 4% accuracy improvement in transferring domains from
Waymo and nuScenes to KITTI. In 3D object detection, it enhances small object
detection by up to 4.37% in AP at moderate difficulty levels in the KITTI
dataset. Even with 90% radial masking, it surpasses baseline models by up to
5.59% in mAP/mAPH across all object classes in the Waymo dataset. Additionally,
our method achieves up to 3.17% and 2.31% improvements in mAP and NDS,
respectively, on the nuScenes dataset, demonstrating its effectiveness with
both single and fused LiDAR-camera modalities.
this https URL

通过提出低成本 LiDAR 感知数据流、径向遮罩自编码等方法，减少能量消耗并提高预测准确性，以便在无人机器人和自主导航中节省传感器的能量和延长电池寿命。