Automation has been a huge driver for the move to all-digital workflows in mapmaking, with automated cartography as the accepted name for this revolution prior to the creation of geographic information systems. To date, the automated storage and processing of geospatial data has greatly improved the speed and efficiency for creating consistently accurate maps and models that can be analyzed for greater insight, but spatial data collection has remained a labor-intensive task.
We’re now seeing the emergence of active mobile sensing via robotic platforms that automate map data collection. This trend for robotic data collection combines with the broader use of sensors in the environment to passively collect and aggregate data to be mapped. Together, mobile and static automated data collection platforms promise to dramatically improve the quality and currency of data on our maps that will usher in a new era of living maps.
The development of unmanned aerial vehicles (UAVs) has been dramatically improved with their successful use in the wars in Afghanistan and Iraq. While the military have been busy developing long-range remote sensing and weapon platforms, there has also been a great deal of development on smaller scale platforms for automated mapping and incident response.
UAVs are a lower-cost aerial mapping platform for many situations, particularly for smaller geographies. While many still require an operator, a great deal of research is being done to make them autonomous. The U.S. Defense Advanced Research Projects Agency (DARPA) is working to combine several drones into an aerial mesh sensor web with individual drones refueled in the air for continuous persistent surveillance. A similar approach is being pursued at the Laboratory of Intelligent Systems of the École Polytechnique Fédérale de Lausanne to create aerial swarms of robots that self-organize for aerial monitoring. And, there’s even a commercial aerial terrain mapping drone by the Belgium-based company Gatewing that automates the aerial collection of imagery and terrain data with little operator input.
Robotic data collection from the air does face some restrictions due to the close regulation of airspace, but small unmanned aircraft are gaining greater favor with regulatory authorities. By making these aerial sensing platforms autonomous, they have the potential to become part of the regular site surveying toolkit for much more rapid collection of landscape and site-level details.
As Michael Goodchild noted in a recent feature for ArcWatch, the potential for meshing interior maps of buildings with the exterior space is a huge frontier that promises to create a seamless fully three-dimensional GIS. As Goodchild noted, the extreme cost of interior mapping is the primary obstacle holding this advancement back. Robots armed with sensors have proven to be a compelling platform for interior data collection, and could prove to be needed approach for driving down the cost and speeding interior data collection.
There are a number of research and real-world applications for robotics use in interior mapping. PenBay Solutions has pioneered spatial robotics for rapid data collection of interior spaces. The University of Queensland has developed lingodroids that autonomously created a shared language about place by playing location games that led them to construct a shared vocabulary for places and created spatial maps. A team effort at Georgia Tech, Cal Tech and the University of Pennsylvania have also developed robots that use lasers and video cameras and on-board data processing called simultaneous location and mapping (SLAM) to create maps.
The idea of autonomous droids scanning and capturing interior information through unassisted teamwork is a compelling vision to make quick and precise work of interior mapping. A great deal of research is going into robotic mapping, because some for of mapping is required for any robot that aims to be autonomous in order for it to navigate its surroundings. Extending this need and capability to mapmaking requires very little additional effort as it’s simply a matter of more thorough data collections of surroundings. At present much of the effort requires a floorplan input, but with lasers and indoor positioning sensors, indoor feature collection at high precision could readily be automated.
Imagine the efficiency improvements of a surveying field crew that might launch an airborne robot as the start of their data collection day, gathering points on the ground while the aerial robot collects data from above, and finishing the day with both an aerial and terrestrial dataset. As laser sensors come down in price, interior robots might be as ubiquitous as Roomba robotic vacuums and could carry out their mapping activity while we sleep to execute detailed interior surveys while buildings are vacant.
A more efficient and automated data collection workflow saves both time and money. With aging populations in the developing world, a more automated approach also would fill capacity gaps while improving data collection capacity. Another element that can’t be ignored is ability of robots to apply great rigor to the data collection task, making precise mapping outcomes accessible to whole new user groups and industries without their need for any training. We still have a long way to go before robots can act as our proxies in the field, but given the great progress to date I’m certain we’ll see this vision play out within the next few decades.