sensor fusion of Udacity

Radar Principles

01 why use Radar

同时Radar能够在多雨多雪的环境下使用

02 the radar sensor

Camera

06 Combining Camera and Lidar

这块投影的公式见知乎willian
我这里结合透视投影做一个介绍:
这个转换的前提是camera的坐标系是：天东北
(1)小孔成像，三维坐标转公制坐标(m)
$$x{}^{\prime }=f\frac{x}{z}$$
$$y{}^{\prime }=f\frac{y}{z}$$

(2) 公制坐标转换成像素坐标(图像中心转到左上角)：
$$(x,y,z{)}^T->f\ast k\ast \frac{x}{z}+C_x,f\ast l\ast \frac{y}{z}+C_y$$
其中k,l为参数：pixel/m, 即多少pixel每米

结合透视投影的例子：
选择图像像素为：800*600
则
$$k=800/(tan(40^o)\ast f\ast 2)pixel/m$$
40是arithum_fov/2

$$l=600/(tan(20^o)\ast f\ast 2)pixel/m$$
20是垂直_fov/2
而如果我们做转换是是需要求
$$f\ast k与f\ast l$$
这样f就约掉了

因此在本例子中需要确认的是：
Lidar的FOV
Lidar水平与垂直的像素点尺寸组合
(图打通lidar像素水平与垂直比例为4:3因此确定像素组合：800600、400300 or 1200*900这个尺寸都是去测试，看效果如何(python的调试工程)
4:3怎么来的就是点数之比：
水平：65/0.16=403
垂直：40/0.13=300

多传感器融合的论文推荐：

Additional Resources on Sensor Fusion and Object Detection & Tracking

Nice work reaching the end of the sensor fusion content! While you still have the project left to do here, we’re also providing some additional resources and recent research on the topic that you can come back to if you have time later on.

Reading research papers is a great way to get exposure to the latest and greatest in the field, as well as expand your learning. However, just like the project ahead, it’s often best to learn by doing - if you find a paper that really excites you, try to implement it (or even something better) yourself!
Optional Reading

All of these are completely optional reading - you could spend days reading through the entirety of these! We suggest moving onto the project first so you have Kalman Filters fresh on your mind, before coming back to check these out.

We’ve categorized these papers to hopefully help you narrow down which ones might be of interest, as well as highlighted a couple key reads by category by including their Abstract section, which summarizes the paper. We’ve also included some additional papers you might consider as well if you want to delve even deeper.
Tracking Multiple Objects and Sensor Fusion

The below papers and resources concern tracking multiple objects, using Kalman Filters as well as other techniques!

No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs by A. Rangesh and M. Trivedi

Abstract: Online multi-object tracking (MOT) is extremely important for high-level spatial reasoning and path planning for autonomous and highly-automated vehicles. In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different
sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks. […]
We demonstrate that our framework is well-suited to track objects through entire maneuvers around the ego-vehicle, some of which take more than a few minutes to complete. We also leverage the modularity of our approach by comparing the effects of including/excluding different sensors, changing the total number of sensors, and the quality of object proposals on the final tracking result.

Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking by R.O. Chavez-Garcia and O. Aycard

Abstract: […] We believe that by including the objects classification from multiple sensors detections as a key component of the object’s representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to
include class information in the core object’s description. Second, we propose a complete perception fusion architecture based on the Evidential framework to solve the Detection and Tracking of Moving Objects (DATMO) problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the
interactIVe IP European project which includes three main sensors: radar, lidar and camera. […]

Stereo cameras

The below papers cover various methods of using stereo camera set-ups for object detection and tracking.

Robust 3-D Motion Tracking from Stereo Images: A Model-less Method by Y.K. Yu, et. al.

Abstract: Traditional vision-based 3-D motion estimation algorithms require given or calculated 3-D models while the motion is being tracked. We propose a high-speed extended Kalman filter-based approach that recovers camera position and orientation from stereo image sequences without prior knowledge as well as the procedure for the reconstruction of 3-D structures. […] The proposed method has been applied to recover
the motion from stereo image sequences taken by a robot and a hand-held stereo rig. The results are accurate compared to the ground truths. It is shown in the experiment that our algorithm is not susceptible to outlying point features with the application of a validation gate.

Vehicle Tracking and Motion Estimation Based on Stereo Vision Sequences by A. Barth
(long read)

Abstract: In this dissertation, a novel approach for estimating trajectories of road vehicles such as
cars, vans, or motorbikes, based on stereo image sequences is presented. Moving objects
are detected and reliably tracked in real-time from within a moving car. […]
The focus of this contribution is on oncoming traffic, while most existing work in the literature
addresses tracking the lead vehicle. The overall approach is generic and scalable
to a variety of traffic scenes including inner city, country road, and highway scenarios. […]
The key idea is to derive these parameters from a set of tracked 3D points on the
object’s surface, which are registered to a time-consistent object coordinate system, by
means of an extended Kalman filter. Combining the rigid 3D point cloud model with
the dynamic model of a vehicle is one main contribution of this thesis. […]
The experimental results show the proposed system is able to accurately estimate the
object pose and motion parameters in a variety of challenging situations, including night
scenes, quick turn maneuvers, and partial occlusions.

Deep Learning-based approaches

The below papers include various deep learning-based approaches to 3D object detection and tracking.

Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net by W. Luo, et. al.

Abstract: In this paper we propose a novel deep neural network that is able to jointly reason about 3D detection, tracking and motion forecasting given data captured by a 3D sensor. By jointly reasoning about these tasks, our holistic approach is more robust to occlusion as well as sparse data at range. Our approach performs 3D convolutions across space and time over a bird’s eye view representation of the 3D world, which is very efficient in terms of both memory and computation. Our experiments on a new very large scale dataset captured in several north american cities, show that we can outperform the state-of-the-art by a
large margin. Importantly, by sharing computation we can perform all tasks in as little as 30 ms.

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection by Y. Zhou and O. Tuzel

Abstract: Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RPN), most existing efforts have focused on hand-crafted feature representations, for example, a bird's eye view projection. In this work, we remove the need of manual feature engineering for 3D point clouds and propose VoxelNet, a generic 3D detection network that unifies feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. […] Experiments on the KITTI car detection benchmark show that VoxelNet outperforms the state-of-the-art LiDAR based 3D detection methods by a large margin. Furthermore, our network learns an effective discriminative representation of objects with various geometries, leading to encouraging results in 3D detection of pedestrians and cyclists, based on only LiDAR.

Other papers on Tracking Multiple Objects and Sensor Fusion

The below papers and resources concern tracking multiple objects, using Kalman Filters as well as other techniques! We have not included the abstracts here for brevity, but you should check those out first to see which of these you want to take a look at.

Multiple Object Tracking using Kalman Filter and Optical Flow by S. Shantaiya, et. al.

Kalman Filter Based Multiple Objects Detection-Tracking Algorithm Robust to Occlusion by J-M Jeong, et. al.

Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques by X. Chen, et. al.

LIDAR-based 3D Object Perception by M. Himmelsbach, et. al

Fast multiple objects detection and tracking fusing color camera and 3D LIDAR for intelligent vehicles by S. Hwang, et. al.

3D-LIDAR Multi Object Tracking for Autonomous Driving by A.S. Rachman (long read)