Solid state lidar is the direction all of the companies jockeying for position in the driverless car market are headed with the exception of Luminar Technologies. Led by a 22 – year old lidar whiz kid this start-up’s technology includes two mirrors and other moving parts that steer a single pencil of laser light around a scene so that a single photodetector can measure the distance to every detail.
“There’s nothing wrong with moving parts,” says Luminar founder and CEO Austin Russell. “There are a lot of moving parts in a car, and they last for 100,000 miles or more.”
He’s got a point there. The challenge is the cost. With $36 million in VC money and 160 employees some people are believers.
The Wisconsin State Cartographers Office will be hosting a second offering of their “Basics of LiDAR” workshop. The workshop will extend over two full days, from 8:30 am to 4:00 pm, on June 12-13, 2017, at UW-Madison.
In this workshop, attendees will get hands-on experience manipulating LiDAR data using industry-standard Esri software. Attendees will gain a broad understanding of: raster data; map algebra and raster data processing; raster data structures; raster DEM characteristics including resolution; LiDAR acquisition and data collection concepts; accuracy and error in the context of LiDAR data; limitations of LiDAR; main LiDAR application domains; bare earth applications of LiDAR; terrain analysis; TINs, breaklines and contours; point clouds and LAS files; and visualization options. Attendees will also work with LAS files, using LASD to display data and create raster files and DEMs.
Registration is limited to 20.
A research team at Oregon State has developed a very fast ground filtering algorithm.
From the Abstract:
Ground filtering is a common procedure in lidar data processing, which separates the point cloud data into ground points and non-ground points. Effective ground filtering is helpful for subsequent procedures such as segmentation, classification, and modeling. Numerous ground filtering algorithms have been developed for Airborne Laser Scanning (ALS) data. However, many of these are error prone in application to TLS data because of its different angle of view and highly variable resolution. Further, many ground filtering techniques are limited in application within challenging topography and experience difficulty coping with some objects such as short vegetation, steep slopes, and so forth. Lastly, due to the large size of point cloud data, operations such as data traversing, multiple iterations, and neighbor searching significantly affect the computation efficiency. In order to overcome these challenges, we present an efficient ground filtering method for TLS data via a Scanline Density Analysis, which is very fast because it exploits the grid structure storing TLS data.
Could be of real value.
A quick update on the Waymo vs. Uber court case.
Last week Uber suffered a setback in their court case when the judge referred the case to the U.S. attorney for an investigation into the possible theft of trade secrets by an Uber executive.
In the ruling, Judge William Alsup said the case must stay in court and not go to a private arbitrator as Uber had wanted.
Waymo, Google’s autonomous car company, sued Uber earlier this year claiming that its former self-driving car expert — Anthony Levandowski — had stolen 14,000 files related to Google’s proprietary LiDAR technology before starting a company, Otto, which Uber bought last summer for $670 million US.
Judge Alsup also has issued a ruling, which remains under seal, on Waymo’s request for an injunction against Uber that would effectively halt its self-driving car testing program. This would be a stumbling block in Uber’s ambitions to develop fully autonomous vehicles.
I had the opportunity today to receive a briefing on Riegl’s impressive Automatic Registration 2.0 software capability. First of all the software now comes loaded on the VZ400i. By making use of the on board digital compass, IMU and GNSS the automatic registration software eliminates the need for targets, if network control is not required. Without targets scan to scan accuracies of 2 to 5 mm are being observed.
If network control is required the need for targets is greatly reduced. For a project of 50 to 100 scans as few as 6 targets would be needed, thereby greatly reducing the need for equipment and the time to set up and retrieve the targets.
For highway projects maximum productivity can be realized by mounting the scanner on a vehicle using something like Certainty 3D’s TopoLift. In this configuration scan times can be held to 3 to 5 minutes which includes the high density scans, images and GNSS position. Registration takes 30 to 40 seconds per position.
The combined hardware and software results in a highly productive system that requires very little training and support since the expertise is built in to the software running on the scanner.
If you are looking for productivity and ROI you need to take a look at the VZ400i with automatic registration.
The purpose of the OpenLSEF initiative is to create a common language describing how features in 3D point clouds should be defined. By establishing definitions and terminology, products from providers can be standardized, designers can expect consistency, self-driving cars can share high-definition maps and tool-makers can focus on ensuring extraction algorithms return expected results. It’s frustrating to be a drafter (or AI) trying to learn what curbs (or kerbs) look like if no one can agree whether flow line or back-of-curb is the defining feature.
OpenLSEF is a user-created initiative focusing on standardizing extraction definition in the AEC (architecture, engineering and construction) field, as well as transmission, utilities and BIM (building information management). These are living standards relating to the meaning of extracted data, as opposed to simply focusing on actual file format standards. As such, OpenLSEF is data-format agnostic and is meaningful whether you deal in DWG, DGN or SHP files.
This effort is certainly a step in the right direction.
This issue of the Cornerstone Report is the USIBD’s second report on Standards in the Building Documentation industry. In particular, this issue looks at the adoption of standards. It has been a couple of years since the USIBD last looked at standards, so they are in a unique position to see how the industry is developing.
The report offers a comparison between the 2017 survey responses and those received in 2015. Questions include are stakeholders aware of standards, how are standards being established and do they use industry standards or create their own?
The adoption of industry standards is in the best interest of all concerned.
The Democratic Republic of Congo has become the first African country to build a national biomass map using LiDAR, furthering the scientific underpinning of their REDD+ processes.
WWF-DRC launched the Carbon Map and Model project in 2012, in partnership with the Ministry of Environment of the DRC and WWF-Germany, and with the support of its partners, University of California, Los Angeles, Southern Mapping Company, and Observatoire Satellital des Forêts d’Afrique Centrale. This project was developed to complete the national forest biomass map through an aerial LiDAR sampling approach.
Each LiDAR sampling site was selected systematically and randomly throughout DRC, incorporating a variety of plots which represent the various Congolese rainforest types. In total, the flight campaign covered 216 sites and over 430,000 ha. All available plot data were compiled and processed to develop a LiDAR biomass model, which was then extrapolated to national scale by satellite imagery, including optical Landsat 8 and active radar data.
The map will support national forest cover monitoring efforts, which include identification of deforested and degraded areas, as well as helping to assess annual carbon emissions from deforestation, and the necessary reporting to the international mechanisms for REDD+. The biomass information from the map has already been integrated into the final Maï-Ndombe Emissions Reduction Program, which is the largest REDD+ program in Africa.
This carbon map will strengthen REDD+ in DRC, providing additional layers of data on forest cover changes and emissions. With tropical deforestation making up nearly 10 percent of global carbon emissions, the success of REDD+ in DRC, where over 60% of Central Africa’s forest is located, is essential to efforts to mitigate global climate change.
The use of multispectral cameras to remotely sense land cover over wide areas has been used for a number of years. More recently the use of multiple wavelength lidar sensors are being studied with the hope of more accurately classifying land cover types.
Researchers at Ryerson University in Canada are reporting impressive results from their use of multispectral lidar sensors that are capable of recording a diversity of spectral reflectance from objects.
They used two different methods to develop land cover classification of an urban study area. The first was image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied.
The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass.
An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively.
As background Waymo, the self-driving car company owned by Google parent company Alphabet, sued Uber in February for patent infringement and theft of trade secrets. Waymo claims that its former employee Levandowski downloaded 14,000 confidential files before leaving to launch the self-driving truck company Otto, which was acquired by Uber. Waymo argues that 14,000 documents are being used to develop Uber’s self-driving technology, while Uber contends that the files never made it onto its servers.
In preparation for a preliminary injunction hearing next month, Waymo filed new claims last week, alleging that Uber withheld information about technology they are using in its development of self-driving cars. Waymo claims Anthony Levandowski, a former Google employee, stole confidential material and used it to jumpstart Uber’s self-driving program.
Uber claimes that its LiDAR system isn’t ready and therefore isn’t being used in its self-driving cars, which rely instead on commercial systems furnished by Velodyne. Waymo now says that Uber worked on a second system that more closely copies Waymo’s designs.
Waymo also claims Uber participated in a “cover up” to keep its second, un-named design hidden from the court. “They were hiding a device,” Waymo’s lawyers said.