Action for perception : active object recognition and pose estimation in cluttered environments

Publication Type:
Thesis
Issue Date:
2017
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Object recognition and localisation are indispensable competency for service robots in everyday environments like offices and kitchens. Presence of similar objects that can only be differentiated from a small part of the surface together with clutter that leads to occlusions make it impossible to detect target objects accurately and reliably from a single observation. When the sensor observing the environment is mounted on a mobile platform, object detection and pose estimation can be facilitated by observing the environment from a series of different viewpoints. Computing Active perception strategies, with the aim of finding optimal actions to enhance object recognition and pose estimation performance is the focus of this thesis. This thesis consists of two main parts: In the first part, it focuses on object detection and pose estimation from a single frame of observation. Using an RGB-D sensor, we propose a modular 3D textured object detection and pose estimation framework which can recognise object under cluttered environment by taking advantage of the geometric information provided from the sensor. To handle less-textured objects and objects under severe illumination conditions, we propose a novel RGB-D feature which is robust to illumination, scale, rotation and viewpoint variations, and provides reliable feature matching results under challenging conditions. The proposed feature is validated for multiple applications including object detection and point cloud alignment. Parts of the above approaches are integrated with existing work to produce a practical and effective perception module for a warehouse automation task. The designed perception system can detect objects of different types and estimate their poses robustly thus guaranteeing a reliable object grasping and manipulation performances. In the second part of the thesis, we investigate the problem of active object detection and pose estimation from two perspectives: with and without considering the uncertainties in the motion model and the observation model. First, we propose a model-driven active object recognition and pose estimation system via exploiting the feature association probability under scale and viewpoint variations. By explicitly modelling the feature association, the proposed system can predict future information more accurately thus laying the foundation of a successful active Next-Best-View planning system even with a naive greedy search technique. We also present a probabilistic framework which handles motion and observation uncertainties in the active object detection and pose estimation problem. We present an optimisation framework which computes the optimal control at each step, using an objective function which incorporates uncertainties in state estimation, feature coverage for better recognition confidence and control consumption. The proposed framework can handle various issues such as object initialisation, collision avoidance, occlusion and changing the object hypothesis. Validations based on a simulation environment are also presented.
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