The past two decades have seen extraordinary growth in the demand for geo-spatial data. This demand has numerous sources and takes many forms; however, the net effect is an ever-increasing thirst for data that is more accurate, has higher density, is produced more rapidly, and is acquired less expensively. Unfortunately, traditional techniques for collecting spatial data, such as conventional surveying techniques, point-wise GPS, or aerial photogrammetry, have difficulties satisfying many of the new data collection requisites. Conventional surveying or point-wise GPS are, for instance, poorly suited for the rapid and inexpensive collection of data over large areas. Traditional aerial photogrammetry, while satisfying these needs, is disadvantaged by the requirement to establish moderately dense and expensive ground control.
An alternative to both point-wise GPS and traditional techniques of data collection is the use of multi-sensor systems that integrate various navigation and remote sensing technologies together on a mobile aerial or terrestrial platform. These Mobile Mapping Systems (MMS) capitalise on the strengths of the individual technologies in order to increase the efficiency of data collection.
A succinct and task-oriented definition of a MMS is as follows: a mobile multi-sensor system used for the rapid collection of directly geo-referenced remotely-sensed data. Key to this definition is the concept of geo-referencing, which refers to the process by which the location and, optionally, attitude, of remote sensors, such as cameras or laser-scanners, is determined in a mapping co-ordinate frame. Once the sensors have been geo-referenced, additional co-ordinate information can then be extracted for features visible in the remotely sensed data. When the remote sensors are directly georeferenced, their locations are determined without reference to external control.
The above definition strongly hints at the components of a MMS. Firstly, a MMS must be mobile. This means it must be mounted on a moving platform such as an aircraft, automobile, or person. Secondly, a MMS must obviously have one or more remote sensors. Common sensors used in MMS include:
• Frame imaging sensors, both film-based and digital
• Laser scanners (LIDAR)
• Synthetic aperture RADAR (SAR)
• Line-imaging sensors
• Hyper and multi-spectral scanners
• Infrared cameras
Finally, in order for these sensors to be directly georeferenced, navigation sensors must also be installed on the platform. The data from the navigation sensors, together with the calibrated or measured positional and angular offsets to the remote sensors, is used to determine the position and attitude of the centres of the remote sensors. Like the remote sensors, the navigation sensors can vary; however, a GPS receiver is omnipresent. Other navigation sensors commonly found on MMS include the following:
• Inertial measurement units
• Tilt sensors
The most important benefits of MMSs are a reduction in both the time and cost of data collection. They also have a number of additional advantages. For example, both spatial and attribute information can be determined from the remotely sensed data. Furthermore, data can be archived and revisited, permitting additional data collection without additional field campaigns.
MMS find applicability in any project where spatially referenced data is required. In particular, projects in which wide coverage and faster turnaround are important, or those in which attribute data available from the remote sensors is as important as co-ordinates. An application that shares all of these demands is GIS data acquisition. Indeed, this application was, perhaps, the primary motivator behind MMS development. Other applications where MMS have found use include asset monitoring, environmental monitoring, disaster response, and accident investigation.
Applications of MMS extend beyond strictly mapping applications. For example, because MMS can rapidly cover large areas, they greatly facilitate the generation of large-scale 3-D models. This task is made particularly efficient if the MMS combines a laser-scanner with frame imagers. Such models are useful in applications ranging from military to tourism. The georeferenced images from MMS can also be useful on their own, even without additional spatial information being extracted from them. For example, the images for Amazon.com’s new block view, which lets users search for a business and then view an image of that business’s building, were collected using a simple MMS.
In spite of the proven abilities and increasingly widespread adoption of MMS, there are a number of areas where significant improvements can still be made. Foremost among these is in the automated extraction of features from the remotely sensed data. Automated feature extraction has been one of the major research areas in mobile mapping technology since its inception. However, the techniques created so far have nearly all been ad-hoc solutions that work only with data from a single remote sensor, and for a limited number of features captured in specific environments from restricted sensor geometries. The techniques also typically require a moderate level of human monitoring.
More advanced techniques of feature extraction that require less user input and interaction will open up even more applications for mobile mapping technology. Many of the developments in this area will require participation from researchers in robotics, artificial intelligence and computer vision. Closer collaboration with these researchers will undoubtedly benefit both the computer science and mobile-mapping communities.
Another area in MMS where there is room for improvement is in the sharing of data between the navigation and remotely sensed data processing streams. Currently, the sharing of data during processing is done at a rather superficial level: the results from the navigation processing are used to georeference the remotely sensed data, and then the mapping information of interest is extracted from the remotely sensed data. More advanced strategies where the sharing is done at the measurement level and/or in both directions could provide improved accuracy and reliability.
An additional limitation of existing
MMS is with their exclusivity.
With the exception of a few aerial imaging systems, most systems
have been one-off creations that are operated by the companies or institutions that created them. There exists no turnkey MMS that can be easily installed on an arbitrary
platform. If an affordable consumer mobile mapping system were available, it is likely that new applications which are not yet even envisioned would be made possible. Ideally, a consumer system would be modular in nature, accepting different navigation and remote sensors according to customer requirements. This plug-and-play framework would extend to the software, which could optimally make use of the data from all available sensors. Tied to the creation of consumer systems is a reduction in system cost. There would, of course, be some reduction simply from the economies of scale in manufacturing a consumer system, but additional cost savings would have to be found through new sensor technologies.
Compared to the intensive research period of the 1990s, during which many first-generation systems were created, MMS development is proceeding at a somewhat reduced pace. However, important advances are still being made and second-generation systems are starting to appear. These new systems benefit from advances being made in their constituent components. For example, in aerial systems, digital sensors are replacing film cameras, and in terrestrial systems, black-and-white cameras are being replaced by higher-resolution colour cameras. Advances in navigation sensor technologies, for instance, GPS modernisation, the GALILEO system, and MEMs-based inertial sensors, are also having an impact on new systems.
More advanced techniques of feature extraction that require less user input and interaction will open up even more possibilities
MMS development is ongoing because the commercial viability of companies who are providing services with MMSs has been well-demonstrated. Given the ever-increasing demand for spatial data, it is safe to conclude that both MMS and their operators will continue to be commercially successful. The future of MMSs is bright!