arXiv subjects

Sarvesh Mayilvahanan

Publications and source records attributed to Sarvesh Mayilvahanan.

2 recordsLinked to original sources

arXiv2023

Twilight SLAM: Navigating Low-Light Environments

This paper presents a detailed examination of low-light visual Simultaneous Localization and Mapping (SLAM) pipelines, focusing on the integration of state-of-the-art (SOTA) low-light image enhancement algorithms with standard and contemporary SLAM frameworks. The primary objective of our work is to address a pivotal question: Does illuminating visual input significantly improve localization accuracy in both semi-dark and dark environments? In contrast to previous works that primarily address partially dim-lit datasets, we comprehensively evaluate various low-light SLAM pipelines across obscurely-lit environments. Employing a meticulous experimental approach, we qualitatively and quantitatively assess different combinations of image enhancers and SLAM frameworks, identifying the best-performing combinations for feature-based visual SLAM. The findings advance low-light SLAM by highlighting the practical implications of enhancing visual input for improved localization accuracy in challenging lighting conditions. This paper also offers valuable insights, encouraging further exploration of visual enhancement strategies for enhanced SLAM performance in real-world scenarios.

cs.RO↗

arXiv2023

Reshaping Viscoelastic-String Path-Planner (RVP)

We present Reshaping Viscoelastic-String Path-Planner a Path Planner that reshapes a desired Global Plan for a Robotic Vehicle based on sensor observations of the Environment. We model the path to be a viscoelastic string with shape preserving tendencies, approximated by a connected series of Springs, Masses, and Dampers. The resultant path is then reshaped according to the forces emanating from the obstacles until an equilibrium is reached. The reshaped path remains close in shape to the original path because of Anchor Points that connect to the discrete masses through springs. The final path is the resultant equilibrium configuration of the Spring-Mass-Damper network. Two key concepts enable RVP (i) Virtual Obstacle Forces that push the Spring-Mass-Damper system away from the original path and (ii) Anchor points in conjunction with the Spring-Mass-Damper network that attempts to retain the path shape. We demonstrate the results in simulation and compare it's performance with an existing Reshaping Local Planner that also takes a Global Plan and reshapes it according to sensor based observations of the environment.

cs.RO↗