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Currently, annotating ground-truth data is a tedious and manual effort, involving finding the important events of interest and using the human eye to determine objects from LiDAR point cloud images. We present a workflow we developed in MATLAB to alleviate some of the pains associated with labeling point cloud data from a LiDAR sensor and the advantages that the workflow provides to the labeler. ...We present a workflow we developed in MATLAB to alleviate some of the pains associated with labeling point cloud data from a LiDAR sensor and the advantages that the workflow provides to the labeler. We discuss the capabilities of a tool we developed to assist users in visualizing, navigating, and annotating objects in point cloud data, tracking these objects through time over multiple frames, and then using the labeled data for developing machine learning based classifiers.

This paper presents a proof of concept for an algorithm to determine the attitude of a multi-layer laser rangefinder or LiDAR (Light Detection And Ranging) relative to a reference frame given the ability of the LiDAR to make measurements to a planar surface with sufficient and proper excitation.

As for LiDAR SLAM system, it faces great challenges in scenarios where there are repetitive and sparse features. ...This paper proposes a construction method of LiDAR anchor constraints to improve the robustness of the SLAM system in the above challenging environment.

In recent years, vehicle-intelligent road cooperation is gaining an increasing attention from both academia and industry, which require deployment of a large scale of road side sensors such as lidar and camera. For the road side sensors, calibration is indispensable to obtain transformation between sensor coordinate frame and geographic coordinate frame. ...To simplify the calibration task and improve efficiency, an automatic calibration method for road side lidar and camera using a planar calibration target is proposed in this paper. The feature of planar target is designed to be easily identified by the sensors, and an Integrated Navigation System (INS), which acquires the geographic coordinate of itself in real time at an accuracy of centimeter level, is fixed on the target. ...The experiment results show that the proposed calibration method is able to calibrate road side lidar and camera efficiently and accurately.

This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. ...The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event.

The proposed system employs Light Detection and Ranging (LIDAR) and passive electro-optics equipment installed in two non-collocated components. The source component consists of a tuneable small-size and low-cost/weight LIDAR emitter, which can be installed either on airborne or ground-based autonomous vehicles, or in fixed surface installations. ...The source component consists of a tuneable small-size and low-cost/weight LIDAR emitter, which can be installed either on airborne or ground-based autonomous vehicles, or in fixed surface installations. ...The proposed bistatic system determines the column-averaged molecular and aerosol pollutant concentrations along the LIDAR beam by measuring the cumulative absorption and scattering phenomena along the optical slant range.

This paper describes the methods used to collect and reduce wake turbulence data behind two distinct types of tiltrotor aircraft using a Light Detection and Ranging (LIDAR) measurement system, which uses laser velocimetry to measure the velocity of dust particles in air that has been disturbed by the passage of an aircraft. ...The MIT LL LIDAR system measured the wake vortices with minimal pre-test preparation; we obtained a large quantity of high quality data in only a few days of testing.

The shared memory interface conveys arbitrary numerical data, RGB image data, and point cloud data for the simulation of LiDAR sensors. The interface consists of a plugin for Unreal Engine, which contains the necessary read/write functions, and a beta toolbox for MATLAB, capable of reading and writing from the same shared memory locations specified in Unreal Engine, MATLAB, and Simulink. ...The interface consists of a plugin for Unreal Engine, which contains the necessary read/write functions, and a beta toolbox for MATLAB, capable of reading and writing from the same shared memory locations specified in Unreal Engine, MATLAB, and Simulink. The LiDAR sensor model was tested by generating point clouds with beam patterns that mimic Velodyne HDL-32E (32 beam) sensors and is demonstrated to run at sufficient frame rates for real-time computations by leveraging the Graphics Processing Unit (GPU).

Camera and a one-beam type Lidar are implemented together with Raspberry Pi. OpenCV free open source software is also used for developing lane detection and object recognition.

Hardware mounted on the roof that are used to support cameras and LIDAR sensors, and mechanisms meant to keep that hardware clean and functioning, add even further noise contamination that can pollute the voice interaction.

In this paper, we introduce one imu radar loosely coupled SLAM method based on our 4D millimeter-wave image radar which it outputs pointcloud containing xyz position information and power information in our autonomous vehicles. at common pointcloud-based slam such as lidar slam usually adopt imu-lidar tightly coupled structure, which slam front end outputs odometry reversly affect imu preintegration. slam system badness occurs when front end odometry drift bigger and bigger or one frame pointcloud match failed. so in our method, we decouple imu and radar odometry crossed relationship, fusing imu and wheel odometry to generate one rough pose trajectory as initial guess value for front end registration, not directly from radar estimated odometry pose, that is to say, front end registration is independent of imu preintegration. besides, we empirically propose one idea juding front end registration result to identify match-less environment and adopt relative wheel odometry pose instead of registration pose when match belief value(mbv) is false. this can handle some degrade environment, such as two-side similar greenbelt. finally, to increase loop detection robustness, we propose two-stage loop detection verify method. first stage is RS(radius search) method, if it passes loop verify, not enter second stage, otherwise enter SC(scan context) second stage, after two stage loop, most real loop can be detected by our slam system. based on above ideas, at multi scene’s datasets, office park, residential area, open road, underground parkingplace etc, we can run our slam system successfully, meanwhile at our office park dataset we compare trajectory precision with tightly-coupled slam structure and the detected loop number with one stage loop method, exprimental result proved our proposed method is valid.

Multi-sensor data fusion from the key sensors such as camera, radar, and lidar is used to achieve the environmental perception for autonomous vehicles by capturing the various attributes of the environment.

The framework leverages the coupling of LiDAR and Inertial Measurement Unit (IMU) to create a point cloud map within the parking environment.

While good results have been obtained by others using a Total Station or LiDAR to physically measure locations for such purposes, this approach could prove difficult if a site of interest is inaccessible (e.g. on a busy highway that cannot be shut down) or if relevent features of the target location have changed (e.g. due to construction or even restriping of lanes lines).

Recently, various calibration techniques proposed for extrinsic calibration for Autonomous Vehicles (AVs) mostly rely on the camera 2D images and depth map to calibrate the 3D LiDAR points. While most methods work with the whole frame, some methods use the objects in the frame to perform the calibration.

October 29-31, 2019 │ Novi, MI

October 29-31, 2019 │ Novi, MI

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