A unified framework for data association aware robust belief space planning and perception. (February 2018)

• Record Type: Journal Article
• Authors: Pathak, Shashank; Thomas, Antony; Indelman, Vadim
• Is Part Of: International journal of robotics research. Volume 37:Number 2/3(2018)
• Page Start: 287
• Page End: 315
• Publication Date: 2018-02
• Subjects: Localization -- mobile and distributed SLAM -- mapping -- autonomous agents -- cognitive robotics -- AI reasoning methods; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364918759606
• Languages: English
• ISSNs: 0278-3649

Abstract

We develop a belief space planning approach that advances the state of the art by incorporating reasoning about data association within planning, while considering additional sources of uncertainty. Existing belief space planning approaches typically assume that data association is given and perfect, an assumption that can be harder to justify during operation in the presence of localization uncertainty, or in ambiguous and perceptually aliased environments. By contrast, our data association aware belief space planning (DA-BSP) approach explicitly reasons about data association within belief evolution owing to candidate actions, and as such can better accommodate these challenging real-world scenarios. In particular, we show that, owing to perceptual aliasing, a posterior belief can become a mixture of probability distribution functions and design cost functions, which measure the expected level of ambiguity and posterior uncertainty given candidate action. Furthermore, we also investigate more challenging situations, such as when prior belief is multimodal and when data association aware planning is performed over several look-ahead steps. Our framework models the belief as a Gaussian mixture model. Another unique aspect of this approach is that the number of components of this Gaussian mixture model can increase as well as decrease, thereby reflecting reality more accurately. Using these and standard costs (e.g. control penalty, distance to goal) within the objective function yields a general framework that reliably represents action impact and, in particular, is capable of active disambiguation. Our approach is thus applicable to both robust perception in a passive setting with data given a priori and in an active setting, such as in autonomous navigation in perceptually aliased environments. We demonstrate key aspects of DA-BSP in a theoretical example, in a Gazebo-based realistic simulation, and also on the real robotic platform using a Pioneer robot in an office environment.