A Minimal Representation of a Map to Localize and Navigate through an Indoor Environment

Localization and Navigation in an indoor environment are one of the basic problems in the area of robotics. Most of the recent SLAM or SfM based mapping systems use low level features (generally SIFT or SURF features) to infer the 3D structure of the environment and estimates it’s location with respect to the inferred 3D structure. The size of the map becomes large with the increase in the size of the environment. Such heavy maps cannot be loaded into robots having a low memory constraint and therefore estimating a memory efficient map representation is interesting in these scenarios. In this paper we explore some techniques to estimate a minimal representation of a map to navigate and localize through an indoor environment without much loss of accuracy. There are two methods to navigate from one place to another, behavior/affordance based navigation (Figure 1) and map based navigation (Figure 2). In the case of behavior/affordance based navigation, we do not create a map of the environment and do not do localization and path planning. It is based on the belief that there exist a particular behavior based solution to a particular navigation problem. For example, to navigate from room A to room B one can design a left wall follower and a room B detector [25]. The decision in this case is made on the all possible actions available in the local environment and then that action is performed. Another way to navigate is through map based navigation. Localization is a necessary building block for navigating a robot from one room to another using map based navigation. Through localization a robot can identify its current position, plan its path through the map and detect if is has reached the goal. However map based navigation requires continuous localization of a robot to identify its location with respect to the map. We believe that a proper balance between the map based navigation and behavior based navigation can lead to a minimal representation of a map by filling the gaps using local behavior based navigation. A better behavior based navigation requires a higher spatial understanding of the environment. For example, if a robot can understand the local structure of the room and recognize the gates and corridors then each node in the map can represent a particular location and the robot can navigate through the map using the local behavior based navigation by recognizing gates and corridors to reach the destination. Using topological map to represent the map can be particularly useful where each node represents an entity (bag of features, 3D structure etc.) used to localize a robot in the global map and edge connect one node to another which is navigated using behavior based navigation. Below we discuss the requirements of behavior/affordance based and map based navigation techniques. Holistic spatial understanding of the environment is a pre-requisite for behavior based navigation tasks. Including semantic spatial knowledge can greatly enhance the performance of robots by providing a more meaningful representation for performing complex tasks. Semantic representation of the environment leads to the formation of spatial primitives each having specific properties and which excites the desired set of behaviors from the robot. Analogously if the robot can understand higher spatial primitive then the map can be built using these spatial primitives itself. This results in memory efficient techniques for building a map where we represent landmarks using higher level spatial primitives instead of lower level features. In this paper, we explore contemporary works in the relevant area of high level scene understanding for behavior based navigation and the map representations which can encode these features. We also propose a technique to create a minimal representation for a map to localize and navigate through an indoor environment.