In this paper, we propose three sets of multi-channel image patch features for monocular visual-IMU (Inertial Measurement Unit) odometry. The proposed feature sets extract image patch exemplars from multiple feature maps of an image. We also modify an existing visual-IMU odometry framework by using different salient point detectors and feature sets and replacing the inlier selection approach with a self-adaptive scheme. The modified framework is used to examine the proposed feature sets. In addition to the Root Mean Square Error (RMSE) metric, we use the Hausdorff distance to measure the inconsistency between the estimated and ground-truth trajectories. Compared to the point-wise comparison used by RMSE, the Hausdorff distance takes the shape inconsistency of two trajectories into account and is hence more perceptually consistent. Experimental results show that the multi-channel feature sets outperform, or perform comparably to, the single gray level channel feature sets examined in this study. Particularly, the multi-channel feature set that uses integral channels, i.e., ICIMGP (Integral Channel Image Patches), outperforms two state-of-the-art feature sets: SIFT (Scale Invariant Feature Transform) and SURF (Speed Up Robust Features). Besides, ICIMGP performs better than the two multi-channel feature sets that are designed based on derivative channels and gradient channels respectively. These promising results are attributed to the fact that the multi-channel features encode richer image characteristics than their single gray level channel counterparts.