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About

Shashi Poddar is a robotics and navigation researcher whose work sits at the intersection of sensor fusion, inertial sensing, and vision-based localization. His research focuses primarily on autonomous navigation systems, attitude estimation, and visual odometry — areas critical to the reliable operation of unmanned vehicles and mobile robots, particularly in GPS-denied environments. Among his most influential contributions is a least square estimation-based adaptive complementary filter for attitude estimation (2018, 24 citations), which addresses the challenge of using low-cost MEMS inertial sensors for accurate orientation tracking in robotic and navigational platforms. His work on accelerometer calibration using particle swarm optimization (2017, 16 citations) further demonstrates his commitment to improving the practical utility of affordable sensors in commercial applications. Poddar has also made notable strides in visual odometry, authoring multiple widely referenced surveys and technical works on camera-based motion estimation (2018, 12 citations each), helping to map the evolution and current trends in the field. His research on visual-inertial odometry (VIO), including the recent AWL-VIO framework, reflects a continued drive to refine optimization-based navigation architectures. Collectively, his work has garnered over 70 citations, establishing him as a meaningful contributor to the autonomous systems community.