Abstract

This paper introduces the Omnidirectional Tractable Three Module Robot for traversing inside complex pipe networks. The robot consists of three omnidirectional modules fixed 120 apart circumferentially which can rotate about their axis allowing holonomic motion of the robot. Holonomic motion enables the robot to overcome motion singularity when negotiating T-junctions and further allows the robot to arrive in a preferred orientation while taking turns inside a pipe. The singularity region while negotiating T-junctions is analyzed to formulate the geometry of the region. The design and motion capabilities are validated by conducting simulations in MSC ADAMS on a simplified lumped-model of the robot.

Abstract

Autonomous navigation and obstacle avoidance are core capabilities that enable robots to execute tasks in the real world. We propose a new approach to collision avoidance that accounts for uncertainty in the states of the agent and the obstacles. We first demonstrate that measures of entropy—used in current approaches for uncertainty-aware obstacle avoidance—are an inappropriate design choice. We then propose an algorithm that solves an optimal control sequence with a guaranteed risk bound, using a measure of overlap between the two distributions that represent the state of the robot and the obstacle, respectively. Furthermore, we provide closed form expressions that can characterize the overlap as a function of the control input. The proposed approach enables modelpredictive control framework to generate bounded-confidence control commands. An extensive set of simulations have been conducted in various constrained environments in order to demonstrate the efficacy of the proposed approach over the prior art. We demonstrate the usefulness of the proposed scheme under tight spaces where computing risk-sensitive control maneuvers is vital. We also show how this framework generalizes to other problems, such as object-following.

Abstract

In this work, we propose adaptive driving behaviors for simulated cars using continuous control deep reinforcement learning. Deep Deterministic Policy Gradient(DDPG) is known to give smooth driving maneuvers in simulated environments. Unfortunately, simple feedforward networks, lack the capability to contain temporal information, hence we have used its Recurrent variant called Recurrent Deterministic Policy Gradients. Our trained agent adapts itself to the velocity of the traffic. It is capable of slowing down in the presence of dense traffic, to prevent collisions as well to speed up and change lanes in order to overtake when the traffic is sparse. The reasons for the above behavior, as well as, our main contributions are: 1. Application of Recurrent Deterministic Policy Gradients. 2. Novel reward function formulation. 3. Modified Replay Buffer called Near and Far Replay Buffers, wherein we maintain two replay buffers and sample equally from both of them.

Abstract

Classical Image-Based Visual Servoing (IBVS) makes use of geometric image features like point, straight line and image moments to control a robotic system. Robust extraction and real-time tracking of these features are crucial to the performance of the IBVS. Moreover, such features can be unsuitable for real world applications where it might not be easy to distinguish a target from rest of the environment. Alternatively, an approach based on complete photometric data can avoid the requirement of feature extraction, tracking and object detection. In this work, we propose one such probabilistic model based approach which uses entire photometric data for the purpose of visual servoing. A novel image modelling method has been proposed using Student Mixture Model (SMM), which is based on Multivariate Student’s t-Distribution. Consequently, a vision-based control law is formulated as a least squares minimisation problem. Efficacy of the proposed framework is demonstrated for 2D and 3D positioning tasks showing favourable error convergence and acceptable camera trajectories. Numerical experiments are also carried out to show robustness to distinct image scenes and partial occlusion.

Abstract

Abstract— We propose a novel pipeline that blends encodings from natural language and 3D semantic maps obtained from visual imagery to generate local trajectories that are executed by a low-level controller. The pipeline precludes the need for a prior registered map through a local waypoint generator neural network. The waypoint generator network (WGN) maps semantics and natural language encodings (NLE) to local waypoints. A local planner then generates a trajectory from the ego location of the vehicle (an outdoor car in this case) to these locally generated waypoints while a low-level controller executes these plans faithfully. The efficacy of the pipeline is verified in the CARLA simulator environment as well as on local semantic maps built from real-world KITTI dataset. In both these environments (simulated and real-world) we show the ability of the WGN to generate waypoints accurately by mapping NLE of varying sequence lengths and levels of complexity. We compare with baseline approaches and show significant performance gain over them. And finally, we show real implementations on our electric car verifying that the pipeline lends itself to practical and tangible realizations in uncontrolled outdoor settings. In loop execution of the proposed pipeline that involves repetitive invocations of the network is critical for any such language-based navigation framework. This effort successfully accomplishes this thereby bypassing the need for prior metric maps or strategies for metric level localization during traversal.

Abstract

Long-short term memory networks(LSTM) models have shown considerable performance on variety of problems dealing with sequential data. In this paper, we propose a variant of Long-Term Recurrent Convolutional Network(LRCN) to detect road intersection. We call this network as IntersectNet. We pose road intersection detection as binary classification task over sequence of frames. The model combines deep hierarchical visual feature extractor with recurrent sequence model. The model is end to end trainable with capability of capturing the temporal dynamics of the system. We exploit this capability to identify road intersection in a sequence of temporally consistent images. The model has been rigorously trained and tested on various different datasets. We think that our findings could be useful to model behavior of autonomous agent in the real-world.

Abstract

Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic participants, but monocular localization demonstrations have been confined to plain roads. We demonstrate — to the best of our knowledge — the first results for monocular object localization and shape estimation on surfaces that are non-coplanar with the moving ego vehicle mounted with a monocular camera. We approximate road surfaces by local planar patches and use semantic cues from vehicles in the scene to initialize a local bundle-adjustment like procedure that simultaneously estimates the 3D pose and shape of the vehicles, and the orientation of the local ground plane on which the vehicle stands. We also demonstrate that our approach transfers from synthetic to real data, without any hyperparameter-/fine-tuning. We evaluate the proposed approach on the KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations. The proposed approach significantly improves the state-of-the-art

Abstract

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

Abstract

We propose a novel motion segmentation formulation over spatio-temporal depth images obtained from stereo sequences that segments multiple motion models in the scene in an unsupervised manner . The motion segmentation is obtained at frame rates that compete with the speed of the stereo depth computation. This is possible due to a decoupling framework that first delineates spatial clusters and subsequently assigns motion labels to each of these cluster with analysis of a novel motion graph model. A principled computation of the weights of the motion graph that signifies the relative shear and stretch between possible clusters lends itself to a high fidelity segmentation of the motion models in the scene. Keywords: Motion

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.