(Re)Imagining Relationality: Brad Isaacs and The Map of the Empire

The exhibition, The Map of the Empire (30 March – 6 May, 2016), featured photography, video, and installation works by Toronto-based artist, Brad Isaacs (Mohawk | mixed heritage). The majority of the artworks within the exhibition were produced from the Canadian Museum of Nature’s research and collections facility (Gatineau, Québec). The Canadian Museum of Nature (CMN), is the national natural history museum of (what is now called) Canada, with its galleries located in Ottawa, Ontario. The exhibition was the first to open at the Centre for Indigenous Research Creation at Queen’s University under the supervision of Dr. Dylan Robinson. Through the installment of The Map of the Empire, Isaacs effectively claimed space on campus grounds – within the geopolitical space of Katarokwi | Kingston – and pushed back against settler colonial imaginings of natural history. The Map of the Empire explored the capacity of Brad’s artistic practice in challenging the general belief under which natural history museums operate: that the experience of collecting/witnessing/interacting with a deceased and curated more-than-human animal will increase conservation awareness and facilitate human care towards nature. The exhibition also featured original poetry by Cecily Nicholson, author of Triage (2011) and From the Poplars (2014), as a response to Brad’s artwork. I locate the work of The Map of the Empire within the broader context of curatorship as a political practice engaging with conceptual and actualized forms of slow violence, both inside of and beyond the museum space. By unmapping the structures of slow, showcased and archived violence within the natural history museum, we can begin to radically transform and reimagine our connections with more-than-humans and encourage these relations to be reciprocal rather than hyper-curated or preserved.

Axis Mapping: The Estimation of Surface Orientations and its Applications in Vehicle Localization and Structural Geology

The map representation of an environment should be selected based on its intended application. For example, a geometrically accurate map describing the Euclidean space of an environment is not necessarily the best choice if only a small subset its features are required. One possible subset is the orientations of the flat surfaces in the environment, represented by a special parameterization of normal vectors called axes. Devoid of positional information, the entries of an axis map form a non-injective relationship with the flat surfaces in the environment, which results in physically distinct flat surfaces being represented by a single axis. This drastically reduces the complexity of the map, but retains important information about the environment that can be used in meaningful applications in both two and three dimensions. This thesis presents axis mapping, which is an algorithm that accurately and automatically estimates an axis map of an environment based on sensor measurements collected by a mobile platform. Furthermore, two major applications of axis maps are developed and implemented. First, the LiDAR compass is a heading estimation algorithm that compares measurements of axes with an axis map of the environment. Pairing the LiDAR compass with simple translation measurements forms the basis for an accurate two-dimensional localization algorithm. It is shown that this algorithm eliminates the growth of heading error in both indoor and outdoor environments, resulting in accurate localization over long distances. Second, in the context of geotechnical engineering, a three-dimensional axis map is called a stereonet, which is used as a tool to examine the strength and stability of a rock face. Axis mapping provides a novel approach to create accurate stereonets safely, rapidly, and inexpensively compared to established methods. The non-injective property of axis maps is leveraged to probabilistically describe the relationships between non-sequential measurements of the rock face. The automatic estimation of stereonets was tested in three separate outdoor environments. It is shown that axis mapping can accurately estimate stereonets while improving safety, requiring significantly less time and effort, and lowering costs compared to traditional and current state-of-the-art approaches.