Histogram Refined Local Ternary Pattern-based Bilateral LPP for Vision Sensor-based Robot Navigation Guidance under Challenging Environments

Abstract

The present letter proposes a new variant of bilateral locality preserving projection (BLPP), a nonlinear manifold learning technique, for vision sensor-based robot navigation guidance in challenging environments, suffering from both variations in ambient illumination levels and vision sensor noises and uncertainties in image acquisition. In its basic form, the adjacency graph in BLPP is formulated both in feature subspace and spatial subspace simultaneously. The present work proposes to compute the feature weights using local ternary pattern (LTP), a lesser noise-sensitive feature descriptor that is also robust against illumination variations. In addition, histogram refinement has been performed on the feature descriptor to encode the local neighborhood structure of the data, proposing a further augmented version of BLPP named histogram refined local ternary pattern-based BLPP (HRLTP-BLPP). Extensive real-life experiments with challenging photometric conditions and sensor noises have been performed to show the usefulness of HRLTP-BLPP over other competing, state-of-the-art approaches utilized for robot navigation guidance.