resolve issues in project pages

_pages/project-pages/2015/Phani_An-Improved-Compliant.md

@@ -3,19 +3,19 @@ layout: project-page-new
 title: "An Improved Compliant Joint Design of a Modular Robot for Descending Big Obstacles"
 authors:
   - name: S Phani Teja
-    sup: #
+    sup: 1
   - name: Sri Harsha
-    sup: #
+    sup: 1
   - name: Avinash Siravuru
-    sup: #
+    sup: 1
   - name: Suril V. Shah
-    sup: #
+    sup: 1
   - name: K Madhava Krishna
-    sup: #
+    sup: 1
 affiliations:
   - name: Robotics Research Center, IIIT Hyderabad, India
     link: https://robotics.iiit.ac.in
-    sup: #
+    sup: 1
 permalink: /publications/2022/Phani_An-Improved-Compliant/
 abstract: "This work focuses on enhancing step descending ability of the modular robot proposed in [16]. The proposed robot consists of three modules connected with each other through passive joints. It is propelled using an active pair of wheels per module. Since there are no actuators at the joints, the joints are not susceptible to losing operability while traversing on rugged terrain. However with the absence of actuators, we face the issue of the robot toppling over when an abnormally large obstacle is encountered. This shortcoming is overcome with the use of compliant joints. The compliant joints are designed by employing springs of optimal stiffness, which is calculated through an optimization formulation aided with the constraints presented by the static analysis of the robot. The novelty lies in the systematic design of compliant joint for step descent. The robot is successful in climbing and descending obstacles of dimension 17 cm. Simulations of the mathematically modelled robot are carried out. The results from the same are validated on a working prototype and presented."
 paper: https://dl.acm.org/doi/pdf/10.1145/2783449.2783518

_pages/project-pages/2017/Krishnam_Small-obstacle.md

@@ -1,25 +1,21 @@
 ---
 layout: project-page-new
-title: "Small obstacle detection using stereo vision for autonomous
-ground vehicle"
+title: "Small obstacle detection using stereo vision for autonomous ground vehicle"
 authors:
   - name: Krishnam Gupta
-    sup: #
+    sup: 1
   - name: Sarthak Upadhyay
-    sup: #
+    sup: 1
   - name: Vineet Gandhi
-    sup: #
+    sup: 1
   - name: K. Madhava Krishna
-    sup: #
+    sup: 1
 affiliations:
   - name: IIIT Hyderabad, India
     link: https://robotics.iiit.ac.in
-    sup: #
+    sup: 1
 permalink: /publications/2017/Krishnam_Small-obstacle/
-abstract: "Small and medium sized obstacles such as rocks, small boulders,
-bricks left unattended on the road can pose hazards for autonomous as well as human driving situations. Many times these objects are too small on the road and go unnoticed on depth and point cloud maps obtained from state of the art range sensors such as 3D LIDAR. We propose a novel algorithm that fuses both appearance and 3D cues such as image gradients, curvature potentials and depth variance into a Markov Random Field (MRF) formulation that segments the scene into obstacle and non obstacle regions. Appearance and depth data obtained from a ZED stereo pair mounted on a Husky robot is used for this purpose. While identifying true positive obstacles such as rocks, large stones accurately our algorithm is
-simultaneously robust to false positive sources such as appearance
-changes on the road, papers and road markings. High accuracy detection in challenging scenes such as when the foreground obstacle blends with the background road scene vindicates the ecacy of the proposed formulation."
+abstract: "Small and medium sized obstacles such as rocks, small boulders, bricks left unattended on the road can pose hazards for autonomous as well as human driving situations. Many times these objects are too small on the road and go unnoticed on depth and point cloud maps obtained from state of the art range sensors such as 3D LIDAR. We propose a novel algorithm that fuses both appearance and 3D cues such as image gradients, curvature potentials and depth variance into a Markov Random Field (MRF) formulation that segments the scene into obstacle and non obstacle regions. Appearance and depth data obtained from a ZED stereo pair mounted on a Husky robot is used for this purpose. While identifying true positive obstacles such as rocks, large stones accurately our algorithm is simultaneously robust to false positive sources such as appearance changes on the road, papers and road markings. High accuracy detection in challenging scenes such as when the foreground obstacle blends with the background road scene vindicates the efficacy of the proposed formulation."
 paper: https://dl.acm.org/doi/pdf/10.1145/3132446.3134889
 #video: https://robotics.iiit.ac.in/people/nazrul.athar/SMS/visapp.mp4
 # iframe: https://www.youtube.com/embed/jhjskX4FQwA

_pages/project-pages/2018/Ganesh_Geometric-Consistency.md

@@ -20,7 +20,7 @@ affiliations:
     link: https://mila.quebec/
     sup: 2
 permalink: /publications/2018/Ganesh_Geometric-Consistency/
-abstract: "With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a selfsupervisory signal and propose "Composite Transformation Constraints (CTCs)", that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network
+abstract: "With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a selfsupervisory signal and propose 'Composite Transformation Constraints (CTCs)', that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network
 results in performance competitive to supervised deep VO methods."
 paper: https://arxiv.org/pdf/1804.03789.pdf
 # iframe: https://www.youtube.com/embed/WyW9T2dSbec