  Detecting objects in 3D LiDAR data is a core technology for autonomous
driving and other robotics applications. Although LiDAR data is acquired over
time, most of the 3D object detection algorithms propose object bounding boxes
independently for each frame and neglect the useful information available in
the temporal domain. To address this problem, in this paper we propose a sparse
LSTM-based multi-frame 3d object detection algorithm. We use a U-Net style 3D
sparse convolution network to extract features for each frame's LiDAR
point-cloud. These features are fed to the LSTM module together with the hidden
and memory features from last frame to predict the 3d objects in the current
frame as well as hidden and memory features that are passed to the next frame.
Experiments on the Waymo Open Dataset show that our algorithm outperforms the
traditional frame by frame approach by 7.5%<EMAIL_ADDRESS>and other multi-frame
approaches by 1.2% while using less memory and computation per frame. To the
best of our knowledge, this is the first work to use an LSTM for 3D object
detection in sparse point clouds.
