Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line segment features are more robust in these environments. In this work, we propose to jointly exploit points and lines within the framework of uncertainty driven regression forests. The proposed approach is thoroughly evaluated on three publicly available datasets against several strong state-of-the-art baselines in terms of several different error metrics. Experimental results prove the efficacy of our method, showing superior or on-par state-of-the-art performance.Comment: published as a conference paper at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Ferromagnetic phase transition for the spanning-forest model (q \to 0 limit of the Potts model) in three or more dimensions

We present Monte Carlo simulations of the spanning-forest model (q \to 0 limit of the ferromagnetic Potts model) in spatial dimensions d=3,4,5. We show that, in contrast to the two-dimensional case, the model has a "ferromagnetic" second-order phase transition at a finite positive value w_c. We present numerical estimates of w_c and of the thermal and magnetic critical exponents. We conjecture that the upper critical dimension is 6.Comment: LaTex2e, 4 pages; includes 6 Postscript figures; Version 2 has expanded title as published in PR

Classification of Southern Ocean krill and icefish echoes using random forests

Acknowledgements The authors thank the crews, fishers, and scientists who conducted the various surveys from which data were obtained. This work was supported by the Government of South Georgia and South Sandwich Islands. Additional logistical support provided by The South Atlantic Environmental Research Institute, with thanks to Paul Brickle. PF receives funding from the MASTS pooling initiative (TheMarine Alliance for Science and Technology for Scotland), and their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions. SF is funded by the Natural Environment Research Council, and data were provided from the British Antarctic Survey Ecosystems Long-term Monitoring and Surveys programme as part of the BAS Polar Science for Planet Earth Programme. The authors also thank the anonymous referees for their helpful suggestions on an earlier version of this manuscript.Peer reviewedPostprin

Population, Forest Degradation and Environment: A Nexus

In order to examine the trend and impact of relationship between growth of forest resource and population in West Bengal, a province of India, in the time series data for every ten-year from 1901-1991 this study suggests that the increase of population to forest land in West Bengal is alarming, because the ability of the forest to satisfy the demands is limited by the extent of forest resource of the state. The increasing population in West Bengal makes a negative impact on the forest. Though this impact is not highly significant at present, the long run relationship between density of population and forest area leads to substantial damage of the forest resource causing acute environmental problem of the state in future. Similarly, soil erosion, which is the only natural factor to damage forest resource in West Bengal, has some significant effect, though not highly. Keeping in view of such problems community forest management programme like social forestry or joint forest management seems to a positive step for protecting environmental problem.Bengal forest resource, population pressure on environmental resource, Soil erosion, Distributed lag model, Time series econometrics