Despite significant progress has been made in visual place recognition for mobile robot navigation, challenges remain, especially in changing environments. Recently, a landmark-based visual place description technique has achieved impressive results under conditions of significant environmental and viewpoint changes, raising the interest of the community in it. This technique combines the strengths of object proposals and convolutional neural networks (ConvNets), which are the latest achievements in object detection and deep learning research. The idea is to detect landmarks in an image with an object proposal method and then characterize these landmarks as features (known as ConvNet features) computed by a ConvNet for matching landmarks. Although a large number of object proposal approaches and ConvNet features have been proposed, it remains unclear how to select or combine object proposals and ConvNet features for a landmark-based visual place recognition system. In this paper we conduct a thorough evaluation of 13 state-of-the-art object proposal methods and 13 kinds of modern ConvNet features on six datasets with various environmental and viewpoint changes, in terms of their place recognition accuracy and computational efficiency. Our study identifies the strengths and weaknesses of object proposal methods and ConvNet features with respect to environmental changes. Conclusions drawn from our analysis are expected to be useful for developing landmark-based visual place recognition systems and benefit other related research fields.