Shridhar Khandekar

Papers

Total Citations

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About

Shridhar Khandekar is a researcher focused on advancing autonomous systems through computer vision and robotics, with key contributions in pedestrian safety, mobile robot navigation, and simultaneous localization and mapping (SLAM). His most cited work, “Identification Of Pedestrian Movement And Classification Using Deep Learning For Advanced Driver Assistance System” (2022, 5 citations), addresses the critical challenge of predicting pedestrian behavior in complex, real-world environments like Indian roads—where unpredictable crossings demand nuanced detection to reduce accidents and traffic delays. Khandekar’s research in “SLAM using AD* Algorithm with Absolute Odometry” (2021, 2 citations) enhances mobile robot autonomy for industrial applications, including warehouse automation and smart grid inspection, by improving localization and mapping accuracy. Additionally, his earlier work on “Overview of Optical Flow Technique for Mobile Robot Obstacle Avoidance” (2018, 1 citation) explores visual navigation methods to detect and avoid obstacles using relativistic motion cues. Together, these studies demonstrate Khandekar’s commitment to bridging deep learning and classical robotics for safer, more efficient autonomous systems. His work is particularly notable for its practical focus on Indian traffic contexts and industrial automation, offering foundational insights for students and researchers in intelligent transportation and mobile robotics.