SLAP: Simultaneous Localization and Planning under Uncertainty via Dynamic Replanning in Belief Space

Ali Akbar Agha-Mohammadi, Saurav Agarwal, Sung Kyun Kim, Suman Chakravorty, Nancy M. Amato

Research output: Contribution to journalArticlepeer-review


Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

Original languageEnglish (US)
Article number8479330
Pages (from-to)1195-1214
Number of pages20
JournalIEEE Transactions on Robotics
Issue number5
StatePublished - Oct 2018
Externally publishedYes


  • Belief space
  • mobile robots
  • motion planning
  • partially observable Markov decision process (POMDP)
  • robust
  • rollout
  • uncertainty

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'SLAP: Simultaneous Localization and Planning under Uncertainty via Dynamic Replanning in Belief Space'. Together they form a unique fingerprint.

Cite this