2D LiDAR Map Prediction via Estimating Motion Flow with GRU
It is a significant problem to predict the 2D LiDAR map at next moment for robotics navigation and path-planning. To tackle this problem, we resort to the motion flow between adjacent maps, as motion flow is a powerful tool to process and analyze the dynamic data, which is named optical flow in video processing. However, unlike video, which contains abundant visual features in each frame, a 2D LiDAR map lacks distinctive local features. To alleviate this challenge, we propose to estimate the motion flow based on deep neural networks inspired by its powerful representation learning ability in estimating the optical flow of the video. To this end, we design a recurrent neural network based on gated recurrent unit, which is named LiDAR-FlowNet. As a recurrent neural network can encode the temporal dynamic information, our LiDAR-FlowNet can estimate motion flow between the current map and the unknown next map only from the current frame and previous frames. A self-supervised strategy is further designed to train the LiDAR-FlowNet model effectively, while no training data need to be manually annotated. With the estimated motion flow, it is straightforward to predict the 2D LiDAR map at the next moment. Experimental results verify the effectiveness of our LiDAR-FlowNet as well as the proposed training strategy. The results of the predicted LiDAR map also show the advantages of our motion flow based method.
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Flow EstimationRepresentation LearningSimilar Papers 제목 키워드 기반
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion
3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The pre…
Optical Flow EstimationScene Flow EstimationSelf-Supervised LearningUniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehicles
LiDAR scene flow is the task of estimating per-point 3D motion between consecutive point clouds. Recent methods achieve centimeter-level accuracy on popular autonomous vehicle (AV) datasets, but are typically only traine…
LIDAR Semantic SegmentationAutonomous Vehicles3D Object DetectionPoint CloudsSelf-Supervised Scene Flow Estimation with 4-D Automotive Radar
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently…
Autonomous VehiclesMotion SegmentationScene Flow EstimationSelf-Supervised Learning+1SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation
Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds have emerged. Scene flow inherently separates every scene into multiple moving agents and a large class of points following a…
Motion SegmentationSegmentationSelf-Supervised LearningTARS: Traffic-Aware Radar Scene Flow Estimation
Scene flow provides crucial motion information for autonomous driving. Recent LiDAR scene flow models utilize the rigid-motion assumption at the instance level, assuming objects are rigid bodies. However, these instance-…
Autonomous Drivingobject-detectionObject DetectionScene Flow Estimation+1