【论文阅读】RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

最新推荐文章于 2025-12-21 13:43:57 发布

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最新推荐文章于 2025-12-21 13:43:57 发布 · 1.4k 阅读

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RadarNet是一种新的神经网络架构，它结合了LiDAR和雷达传感器的数据，以实现对场景中动态对象的精确检测和速度估计。通过早期融合利用雷达的长感知范围和几何信息，晚期融合利用雷达的动态信息（如径向速度）进行更精确的目标关联和速度聚合。实验表明，这种方法在检测远距离物体和理解动态物体运动方面提高了感知能力。

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Abstract

Radar provides complementary information in the form of Doppler velocity.

propose a new solution that exploits both LiDAR and Radar sensors for perception.

1 Introduction

cameras capture rich appearance features, LiDAR provides direct and accurate 3D measurements.
Challenges:

sparsity of LiDAR measurements (e.g., at long range)
sensor’s sensitivity to weather (e.g., fog, rain and snow)
estimating their velocities is also of vital importance.
cameras and Lidar provides static information only.

Radar’s limitations:

data is very sparse (typically much more so than LiDAR)
the measurements are ambiguous in terms of position and velocity
the readings lack tangential information and often contain false positives

What this paper do:

design a novel neural network architecture, dubbed RadarNet, which can exploit both LiDAR and Radar to provide accurate detections and velocity estimates for the actors in the scene.
propose a multi-level fusion scheme that can fully exploit both geomet- ric and dynamic information of Radar data.

Steps:

first fuse Radar data with LiDAR point clouds via a novel voxel-based early fusion approach to leverage the Radar’s long sensing range.
Furthermore, after we get object detections, we fuse Radar data again via an attention-based late fusion approach to leverage the Radar’s velocity readings.

attention module transforming the 1D radial velocities from Radar to accurate 2D object velocity estimates.

2 Related Work

3 Review of LiDAR and Radar Sensors

LiDAR (light detection and ranging) sensors can be divided into three main types: spinning LiDAR(旋转激光雷达), solid state LiDAR(固态激光雷达), and flash LiDAR(闪光激光雷达).
In this paper w