The LiDAR Compass: Extremely Lightweight Heading Estimation with Axis Maps

This paper introduces the LiDAR compass, a bounded and extremely lightweight heading estimation technique that combines a two-dimensional laser scanner and axis maps, which represent the orientations of flat surfaces in the environment. Although suitable for a variety of indoor and outdoor environments, the LiDAR compass is especially useful for embedded and real-time applications requiring low computational overhead. For example, when combined with a sensor that can measure translation (e.g., wheel encoders) the LiDAR compass can be used to yield accurate, lightweight, and very easily implementable localization that requires no prior mapping phase. The utility of using the LiDAR compass as part of a localization algorithm was tested on a widely-available open-source data set, an indoor environment, and a larger-scale outdoor environment. In all cases, it was shown that the growth in heading error was bounded, which significantly reduced the position error to less than 1% of the distance travelled.

BARRIERS AND SOLUTIONS TO OPEN SOURCE APPROPRIATE TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT: INNOVATION THROUGH COLLABORATION

It has been proposed that the field of appropriate technology (AT) - small-scale, energy efficient and low-cost solutions, can be of tremendous assistance in many of the sustainable development challenges, such as food and water security, health, shelter, education and work opportunities. Unfortunately, there has not yet been a significant uptake of AT by organizations, researchers, policy makers or the mainstream public working in the many areas of the development sector. Some of the biggest barriers to higher AT engagement include: 1) AT perceived as inferior or ‘poor persons technology’, 2) questions of technological robustness, design, fit and transferability, 3) funding, 4) institutional support, as well as 5) general barriers associated with tackling rural poverty. With the rise of information and communication technologies (ICTs) for online networking and knowledge sharing, the possibilities to tap into the collaborative open-access and open-source AT are growing, and so is the prospect for collective poverty reducing strategies, enhancement of entrepreneurship, communications, education and a diffusion of life-changing technologies. In short, the same collaborative philosophy employed in the success of open source software can be applied to hardware design of technologies to improve sustainable development efforts worldwide. To analyze current barriers to open source appropriate technology (OSAT) and explore opportunities to overcome such obstacles, a series of interviews with researchers and organizations working in the field of AT were conducted. The results of the interviews confirmed the majority of literature identified barriers, but also revealed that the most pressing problem for organizations and researchers currently working in the field of AT is the need for much better communication and collaboration to share the knowledge and resources and work in partnership. In addition, interviews showcased general receptiveness to the principles of collaborative innovation and open source on the ground level. A much greater focus on networking, collaboration, demand-led innovation, community participation, and the inclusion of educational institutions through student involvement can be of significant help to build the necessary knowledge base, networks and the critical mass exposure for the growth of appropriate technology.

Estimating the Heading of a Husky Mobile Robot with a LiDAR Compass based on Direction Maps

Without an absolute position sensor (e.g., GPS), an accurate heading estimate is necessary for proper localization of an autonomous unmanned vehicle or robot. This paper introduces direction maps (DMs), which represent the directions of only dominant surfaces of the vehicle’s environment and can be created with negligible effort. Given an environment with reoccurring surface directions (e.g., walls, buildings, parked cars), lines extracted from laser scans can be matched with a DM to provide an extremely lightweight heading estimate that is shown, through experimentation, to drastically reduce the growth of heading errors. The algorithm was tested using a Husky A200 mobile robot in a warehouse environment over traverses hundreds of metres in length. When a simple a priori DM was provided, the resulting heading estimation showed virtually no error growth.