arXiv subjects

Surya Pratap Singh

Publications and source records attributed to Surya Pratap Singh.

3 recordsLinked to original sources

arXiv2025

GPS-DRIFT: Marine Surface Robot Localization using IMU-GPS Fusion and Invariant Filtering

This paper presents an extension of the DRIFT invariant state estimation framework, enabling robust fusion of GPS and IMU data for accurate pose and heading estimation. Originally developed for testing and usage on a marine autonomous surface vehicle (ASV), this approach can also be utilized on other mobile systems. Building upon the original proprioceptive only DRIFT algorithm, we develop a symmetry-preserving sensor fusion pipeline utilizing the invariant extended Kalman filter (InEKF) to integrate global position updates from GPS directly into the correction step. Crucially, we introduce a novel heading correction mechanism that leverages GPS course-over-ground information in conjunction with IMU orientation, overcoming the inherent unobservability of yaw in dead-reckoning. The system was deployed and validated on a customized Blue Robotics BlueBoat, but the methodological focus is on the algorithmic approach to fusing exteroceptive and proprioceptive sensors for drift-free localization and reliable orientation estimation. This work provides an open source solution for accurate yaw observation and localization in challenging or GPS-degraded conditions, and lays the groundwork for future experimental and comparative studies.

cs.RO↗

arXiv2024

OriCon3D: Effective 3D Object Detection using Orientation and Confidence

In this paper, we propose an advanced methodology for the detection of 3D objects and precise estimation of their spatial positions from a single image. Unlike conventional frameworks that rely solely on center-point and dimension predictions, our research leverages a deep convolutional neural network-based 3D object weighted orientation regression paradigm. These estimates are then seamlessly integrated with geometric constraints obtained from a 2D bounding box, resulting in derivation of a comprehensive 3D bounding box. Our novel network design encompasses two key outputs. The first output involves the estimation of 3D object orientation through the utilization of a discrete-continuous loss function. Simultaneously, the second output predicts objectivity-based confidence scores with minimal variance. Additionally, we also introduce enhancements to our methodology through the incorporation of lightweight residual feature extractors. By combining the derived estimates with the geometric constraints inherent in the 2D bounding box, our approach significantly improves the accuracy of 3D object pose determination, surpassing baseline methodologies. Our method is rigorously evaluated on the KITTI 3D object detection benchmark, demonstrating superior performance.

cs.CV↗

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↗