Anshul Paigwar

I am a research engineer at team Chroma in Inria Grenoble, France, where I work on computer vision and deep learning.

At Inria, I am supervised by Prof. Christian Laugier, mainly focusing on developing robust and real-time perception systems for autonomous vehicles. I deal with large-scale data streams from RGB cameras, LiDARs, and Event-based cameras. I've worked on sensor fusion techniques for 2D and 3D object detection, semantic segmentation, depth estimation, motion forecasting, occupancy grid prediction, collision risk estimation, simulations in CARLA and Gazebo, localization, tracking, and many more applications.

I got my master's in Graphics, Vision, and Robotics from ENSIMAG, a prestigious French Grande École in Grenoble, France. My master thesis was supervised by Prof. Christian Wolf, on exploring visual attention mechanisms for 3D space.

I am an active reviewer at top robotics conferences including ICRA, IROS, IV, ITSC, ICARCV, CIS-RAM.

Email  /  CV  /  Google Scholar  /  Github  /  Website  /  Medium

Along with my research interests, I hold a strong entrepreneurial spirit, I founded Delice Robotics and helped kickstart Ivlabs.

At Délice we built Robotic and AI systems to automate food preparation and vending. The aim was to make quality food more affordable and available to everyone. I worked on multiple facets including project ideation, fundraising (100K euros), team building, prototyping, business model, customer need understanding, product demos & presentation.

I helped kickstart IvLabs! It has grown to be among the top robotics labs in India with over 100 active members. I regularly mentor and manage student projects at IvLabs as my responsibility to impart knowledge and give back to the community.

I'm interested in computer vision, machine learning, and image processing. Much of my research is about inferring the dynamic environment around robot from images and point cloud. Representative papers are highlighted.

A Graph Neural Network is used to predict the centerline that the target intends to follow. Based on that, we predict a distribution over potential endpoints, and multiple lane-oriented trajectory realizations.

Architecture that fuses multi-camera and LiDAR data at different scales to produce bird's eye view semantic grids of the environment.

We leverage 2D object detection to reduce the search space in the 3D. Then use the Pillar Feature Encoding network for object localization in the reduced point cloud. At the time of submission F-Pointpillars ranked among top 5 approches for BEV pedestrian detection on KITTI Dataset.

GndNet uses PointNet and Pillar Feature Encoding network to extract features and regresses ground height for each cell of the grid. GndNet establishes a new state-of-the-art, achieves a run-time of 55Hz for ground plane estimation and ground point segmentation.

We extend the theory of visual attention mechanisms to 3D point clouds and introduce a new recurrent 3D Localization Network module. Rather than processing the whole point cloud, the network learns where to look (finding regions of interest), which significantly reduces the number of points to be processed and inference time.

We propose a redundant sensor fusion model of event-based and frame-based cameras that is robust to common image corruptions. Our sensor fusion approach is over 30% more robust to corruptions compared to only frame-based detections and outperforms the only event-based detection.

STADIE-Net takes advantage of stagewise refinement and prediction of disparity using events from 2 neuromorphic cameras in a stereo setup. The method utilizes voxel grid representation for events as input and proposes a 4 stage network going from coarse to finer disparity prediction.

We propose a spatio-temporal prediction network pipeline that takes the past information from the environment and semantic labels separately to predict occupancy for a longer horizon of 3 seconds and in a relatively complex environment.

We propose new probabilistic models to obtain Stochastic Reachability Spaces for vehicles and pedestrians detected in the scene. We then exploit these probabilistic predictions of the road-users' future positions, along with the expected ego-vehicle trajectory, to estimate the probability of collision risk in real-time.

We present a Statistical Model Checking (SMC) approach to validate the collision risk assessment generated by a probabilistic perception system. SMC represents an intermediate between test and exhaustive verification by relying on statistics and evaluates the probability of (KPIs) based on a large number of simulations.

We fuse a dynamic occupancy grid map (DOGMa) with an object detector. DOGMa is obtained by applying a Bayesian filter on raw sensor data. This improves the tracking of the partially observed / unobserved objects with the help of the Bayesian filter on raw data.

we use dynamic occupancy grid maps to exploit any dynamic information from the driving sequences for 3D object detection. Our results show that having access to the environment dynamics improves by 27% the ability of the detection algorithm to predict the orientation of smaller obstacles such as pedestrians.

We propose a general framework for developing medical assistive robots capable of delivering food and medicine to patients and facilitating teleconferencing with doctors. Autonomous mobility of robot is validated in simulated environments, while a teleoperated prototype was deployed at AIIMS Nagpur, India.

We propose a Spatio-Temporal Conditional Random Field (STCRF) method for ground modeling and labeling in 3D Point clouds, based on a local ground elevation estimation. Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic.

We presents a novel design of a Compliant Omni-directional snake robot (COSMOS) consisting of mechanically and software linked spherical robot modules.

I have always been fascinated by robots; building them and bringing them to life is something I take pleasure in. List of my other projects can be found here.

A new pointwise convolution operator that can be applied at each point of a 3D point cloud. can yield competitive accuracy in both semantic segmentation and object recognition task.

We proposed a new framework for real-time Detection and Tracking for Moving Objects (DATMO) in 3D point clouds. We tested our algorithm with the point cloud from Intel Realsense Camera and Velodyne-16 in indoor environment.

We fused GPS, odometry and IMU data for accurate localisation of the vehicle. We used Constant Heading and Velocity (CHCV) Vehicle Model for the dynamics of the vehicle. Finally the sensor fusion algorithm was tested on Toyota E-COMs experimental autonomous vehicle platform.

For detection in 3D point cloud we first use RANSAC algorithm to find a dominant plane (road). Then use region growing algorithm and orientation of normal to road plane to detect the edges of the road. Finally we fit a cubic spline through the detected points.

This website was forked from source code