16 citations · 25 across the 7 of their papers we have counts for
8 papers
IMU-based Online Multi-lidar Calibration
Sandipan Das, Bengt Boberg, Maurice Fallon +1
Modern autonomous systems typically use several sensors for perception. For best performance, accurate and reliable extrinsic calibration is necessary. In this research, we propose…
Observability-aware online multi-lidar extrinsic calibration
Sandipan Das, Ludvig af Klinteberg, Maurice Fallon +1
Accurate and robust extrinsic calibration is necessary for deploying autonomous systems which need multiple sensors for perception. In this paper, we present a robust system for re…
M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance
Sandipan Das, Navid Mahabadi, Maurice Fallon +1
We present a robust system for state estimation that fuses measurements from multiple lidars and inertial sensors with GNSS data. To initiate the method, we use the prior GNSS pose…
Neural Greedy Pursuit for Feature Selection
Sandipan Das, Alireza M. Javid, Prakash Borpatra Gohain +2
We propose a greedy algorithm to select important features among input features for a non-linear prediction problem. The features are selected one by one sequentially, in a…
Extrinsic Calibration and Verification of Multiple Non-overlapping Field of View Lidar Sensors
Sandipan Das, Navid Mahabadi, Addi Djikic +3
We demonstrate a multi-lidar calibration framework for large mobile platforms that jointly calibrate the extrinsic parameters of non-overlapping Field-of-View (FoV) lidar sensors,…
Multi-modal curb detection and filtering
Sandipan Das, Navid Mahabadi, Saikat Chatterjee +1
Reliable knowledge of road boundaries is critical for autonomous vehicle navigation. We propose a robust curb detection and filtering technique based on the fusion of camera semant…
Statistical model-based evaluation of neural networks
Sandipan Das, Prakash B. Gohain, Alireza M. Javid +2
Using a statistical model-based data generation, we develop an experimental setup for the evaluation of neural networks (NNs). The setup helps to benchmark a set of NNs vis-a-vis m…
A ReLU Dense Layer to Improve the Performance of Neural Networks
Alireza M. Javid, Sandipan Das, Mikael Skoglund +1
We propose ReDense as a simple and low complexity way to improve the performance of trained neural networks. We use a combination of random weights and rectified linear unit (ReLU)…